WO2026036143A1 - Advanced spectral sensing device for biometric diagnostics - Google Patents

Abstract

A spectral sensing device and associated methods for enhanced diagnostic capabilities across medical fields are disclosed. The device incorporates multispectral, hyperspectral, and photoplethysmography technologies to analyze biological features including tongue, oral cavity, saliva, teeth, and other tissues. The system transforms any standard RGB camera into a multispectral imaging device by training machine learning models to associate RGB values with spectral values inside and outside the visible spectrum using measurements from a single pixel detector spectroradiometer. A multimodal neural network with separate processing branches analyzes spectral images, non-spectral images, and tabular data to predict microbiome composition. The system generates health risk assessments by comparing predicted microbial abundances to thresholds associated with various medical conditions. Near-infrared sensing enables blood flow monitoring, subcutaneous imaging, and assessment of physiological parameters. Convolutional neural networks enable spectral reconstruction without requiring the physical device, while supporting continuous monitoring in various form factors.

Description

Attorney Reference No. 444365.000020

ADVANCED SPECTRAL SENSING DEVICE FOR BIOMETRIC DIAGNOSTICS

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of US Provisional Patent Appln. No. 63/681,269 filed on August 9, 2024. The entirety of this application is hereby incorporated herein by reference.

FIELD OF INVENTION

[0002] The present disclosure relates to spectral sensing devices for biometric diagnostics, and more particularly to a multispectral imaging system that combines a single pixel detector spectroradiometer with machine learning models to predict spectral values across visible and non-visible spectra from standard RGB camera images, and a multimodal machine learning system that combines spectral imaging, conventional photography, and user-reported data to predict microbiome composition and other biological markers from non-invasive daily scans.

BACKGROUND

[0003] Multispectral and hyperspectral imaging technologies have expanded beyond traditional RGB imaging by capturing electromagnetic spectra across ultraviolet, visible, and infrared wavelengths. These advanced imaging techniques provide detailed spectral information that can reveal material properties, chemical compositions, and biological characteristics not visible through conventional color photography. Traditional multispectral imaging systems typically employ complex arrangements of prisms, gratings, and filters to separate light into component spectra, while hyperspectral systems generally utilize intricate assemblies of diffraction gratings, interferometers, and imaging spectrometers to capture detailed spectral information across numerous contiguous bands.

[0004] The complexity and cost of conventional multispectral and hyperspectral imaging systems have limited their widespread adoption in many applications. These systems often require specialized optical components, precise mechanical alignment, and controlled environmental conditions to achieve accurate spectral measurements. The resulting equipment tends to be expensive, bulky, and technically demanding to operate, making it impractical for many potential applications where costeffectiveness and ease of use are considerations.

[0005] Standard smartphone cameras and consumer-grade imaging devices capture only three broad spectral channels corresponding to red, green, and blue wavelengths. While this RGB approach provides adequate color reproduction for human visual perception, it lacks the spectral resolution to detect subtle material differences or biological variations that may be present across the broader electromagnetic spectrum. The limited spectral information available from RGB cameras constrains their utility in applications where detailed spectral analysis could provide valuable insights.

[0006] Machine learning and artificial intelligence techniques have shown promise in various imaging applications, including the ability to extract additional information from limited input data. These computational approaches can potentially bridge the gap between simple RGB imaging and complex Attorney Reference No. 444365.000020 multispectral systems by learning relationships between easily captured data and more detailed spectral characteristics. However, training such systems typically requires large datasets of paired measurements linking simple inputs to complex outputs.

[0007] In medical and biological applications, spectral imaging can provide information about tissue composition, blood oxygenation, metabolic activity, and other physiological parameters. Near-infrared wavelengths, in particular, can penetrate biological tissues and reveal information about subsurface structures and processes. The ability to perform such measurements using accessible and cost-effective equipment could enable new approaches to health monitoring and diagnostic applications.

[0008] The integration of spectral sensing capabilities with portable devices and smartphone platforms could democratize access to advanced imaging technologies. Such integration would allow users to benefit from spectral analysis capabilities without requiring specialized equipment or technical expertise. However, achieving this integration while maintaining measurement accuracy and reliability presents technical challenges that have not been fully addressed by existing approaches.

[0009] Traditional microbiome analysis methods present significant barriers to widespread adoption and real-time health monitoring applications. Current approaches typically rely on sample collection kits that require users to collect biological specimens such as stool, saliva, or skin swabs using specialized containers and preservation solutions. These collection procedures may be inconvenient for users and can introduce variability in sample quality depending on collection technique, storage conditions, and time delays before processing.

[0010] The cost structure of conventional microbiome analysis creates additional limitations for routine health monitoring. Laboratory -based sequencing techniques, such as 16S rRNA gene sequencing or whole-genome shotgun sequencing, involve expensive reagents, specialized equipment, and skilled technical personnel. The per-sample costs can range from hundreds to thousands of dollars, making frequent monitoring financially prohibitive for most individuals and limiting the accessibility of microbiome insights to research settings or specialized clinical applications.

[0011] Laboratory dependency represents another significant constraint in traditional microbiome analysis workflows. Samples must be shipped to centralized facilities equipped with DNA extraction equipment, PCR amplification systems, and next-generation sequencing platforms. This requirement introduces logistical complexities, shipping delays, and potential sample degradation during transport. The centralized nature of these facilities may also create bottlenecks during periods of high demand, further extending processing times.

[0012] The temporal aspects of conventional microbiome analysis present challenges for capturing the dynamic nature of microbial communities. Processing times from sample collection to results delivery typically span several weeks to months, depending on laboratory capacity and analysis complexity. During this extended timeframe, the actual microbiome composition may undergo substantial changes Attorney Reference No. 444365.000020 due to dietary modifications, medication use, environmental exposures, or natural fluctuations in microbial populations.

[0013] Data analysis and interpretation in traditional microbiome studies require specialized bioinformatics expertise and computational resources. Raw sequencing data must undergo quality control, taxonomic classification, diversity analysis, and statistical interpretation before meaningful insights can be extracted. These analytical steps often require additional weeks of processing time and may necessitate collaboration with bioinformatics specialists, further extending the timeline from sample collection to actionable results.

[0014] The static nature of traditional microbiome snapshots limits their utility for understanding temporal dynamics and responding to rapid changes in microbial communities. Single time-point measurements may not capture important fluctuations that occur over shorter timescales, such as responses to dietary changes, antibiotic treatments, or acute health conditions. This limitation constrains the ability to provide timely interventions or personalized recommendations based on current microbiome status.

[0015] The development of machine learning approaches that can transform standard RGB camera data into multispectral information represents a potential paradigm shift in spectral imaging accessibility. Traditional approaches to multispectral imaging require dedicated hardware components that capture light across multiple discrete wavelength bands simultaneously or sequentially. These systems may achieve high spectral resolution but at the cost of increased complexity, size, and expense that limits their deployment in consumer applications.

[0016] Recent advances in computational imaging and machine learning have demonstrated the potential for extracting spectral information beyond the native capabilities of standard imaging sensors. These approaches may leverage the relationships between RGB pixel values and broader spectral characteristics that can be learned from training datasets containing paired RGB and multispectral measurements. The trained models may then predict spectral values across extended wavelength ranges based solely on RGB input data, potentially eliminating the hardware requirements for specialized spectral sensors.

[0017] The computational approach to spectral reconstruction may offer several advantages over traditional hardware-based methods. Standard RGB cameras are widely available, cost-effective, and already integrated into numerous consumer devices including smartphones, tablets, and digital cameras. The ubiquity of these devices may enable widespread deployment of spectral analysis capabilities without requiring users to acquire specialized equipment or undergo technical training.

[0018] Machine learning models trained for spectral reconstruction may compensate for various limitations inherent in consumer-grade imaging devices. Standard cameras often apply automatic processing including white balance correction, gamma adjustment, and dynamic range compression that Attorney Reference No. 444365.000020 can alter the relationship between scene radiance and recorded pixel values. The trained models may incorporate correction factors that account for these processing effects, potentially enabling accurate spectral predictions despite the modifications applied by camera firmware.

[0019] The accuracy of computational spectral reconstruction may depend on the comprehensiveness and quality of training datasets used to establish relationships between RGB values and extended spectral information. Training datasets may need to encompass diverse materials, lighting conditions, and imaging scenarios to ensure robust performance across different application contexts. The machine learning models may also require validation across different camera types and manufacturers to ensure consistent performance across various hardware platforms.

[0020] The potential for maintaining diagnostic accuracy while eliminating specialized hardware requirements may enable new applications in medical and biological monitoring. Healthcare providers and researchers may benefit from spectral analysis capabilities that can be deployed using existing imaging infrastructure rather than requiring investment in specialized spectral imaging systems. This accessibility may facilitate broader adoption of spectral analysis techniques in clinical settings and enable patient monitoring applications that were previously impractical due to equipment costs and complexity.

SUMMARY

[0021] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0022] According to an aspect of the present disclosure, a method for increasing a spectral resolution of a color camera to replicate multispectral, hyperspectral and PPG imaging systems is provided. The method comprises capturing, by a red-green-blue ("RGB") color image sensor located at a first position, a plurality of color images of one or more objects, each of the plurality of color images having RGB values within the visible spectrum. The method further comprises collecting, by a single pixel detector ("SPD") spectroradiometer located at the first position, measurements for each of the plurality of color images, the measurements comprising spectral values both inside and outside of the visible spectrum. The method additionally comprises combining each of the plurality of color images and respective measurements to form a training dataset. The method also comprises training, using the training dataset, one or more machine models to associate the RGB values with the respective spectral values both inside and outside of the visible spectrum. The method further comprises predicting, using the one or more machine models, spectral values both inside and outside of the visible spectrum of one or more additional color images based on RGB values. Attorney Reference No. 444365.000020

[0023] According to another aspect of the present disclosure, a multimodal machine learning system for predicting microbiome composition from non-invasive daily scans is provided. The system comprises a data processing pipeline configured to process raw DNA sequencing files from biological samples to establish ground-truth microbiome profiles. The system further comprises a data aggregation component configured to combine the ground-truth microbiome profiles with time-series data from daily scans within a specified time window. The daily scans comprise spectral images, conventional photographs, and user-reported data collected via a mobile application. The system additionally comprises a multimodal having three parallel input branches. A first branch comprises a convolutional neural network configured to process spectral images. A second branch comprises a pre-trained ResNet model configured to process conventional photographs. A third branch comprises a feed-forward network configured to process tabular user data. In some examples, the tabular user data branch is omitted, and predictions are based on only spectral images and/or conventional photographs. The system also comprises a prediction head configured to receive concatenated features from the three parallel input branches and output predicted abundance values for target bacteria.

[0024] According to other aspects of the present disclosure, the multimodal machine learning system may include one or more of the following features. The data processing pipeline may use taxonomic classification tools such as Kraken2, MetaPhlAn, or similar bioinformatics platforms to determine species-level abundance for each biological sample. The time window may be plus or minus 30 days from a ground-truth sample date. The spectral images may be single-channel images captured by a proprietary spectral device. The conventional photographs may be RGB images of a user's tongue. The user-reported data may include demographic information, lifestyle data, and daily questionnaire responses. The convolutional neural network may process 64x64, 128x128, 256x256 or other suitable dimensions spectral images. The pre-trained ResNet model may be ResNetl8 fine-tuned on tongue photographs. The feed-forward network may be a multi-layer perceptron. The system may be configured to train separate models for different target bacteria. The system may achieve a Mean Absolute Error of 0.12 or less for Streptococcus salivarius prediction. The system may be configured to perform userholdout validation where models are tested on entirely unseen users.

[0025] A significant aspect of the disclosure enables any standard RGB camera to function as a multispectral imaging device through trained machine learning models, eliminating the need for specialized spectral hardware while maintaining diagnostic accuracy.

[0026] The foregoing general description of the illustrative examples and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive. Attorney Reference No. 444365.000020

BRIEF DESCRIPTION OF DRAWINGS

[0027] Other objects and advantages of the present disclosure will become apparent to those skilled in the art upon reading the following detailed description of examples and appended claims, in conjunction with the accompanying drawings, in which like reference numerals have been used to designate like elements, and in which:

[0028] FIG. 1 A is a graph showing RGB values of original images, according to an aspect of the present disclosure;

[0029] FIG. IB is a graph showing RBG values using a look-up table (LUT) 2nd-degree polynomialbased method for image color correction, according to an aspect of the present disclosure;

[0030] FIG. 2A is a chart showing RGB values of an original camera image, according to an aspect of the present disclosure;

[0031] FIG. 2B is a chart showing RGB values in an image corrected by the LUT polynomial method, according to an aspect of the present disclosure;

[0032] FIG. 2C is a reference color checker, according to an aspect of the present disclosure;

[0033] FIG. 3 A is a chart showing values for a reconstructed image, according to an aspect of the present disclosure;

[0034] FIG. 3B is a chart showing a reference color checker, according to an aspect of the present disclosure;

[0035] FIG. 4A is a graph showing RGB values from a camera with the color correction procedure, according to an aspect of the present disclosure;

[0036] FIG. 4B is a graph showing RGB values from the SPD sensor, according to an aspect of the present disclosure;

[0037] FIG. 5A is a chart showing RGB values from a reference image, according to an aspect of the present disclosure;

[0038] FIG. 5B is a chart showing RGB values from the RandomForest model, according to an aspect of the present disclosure;

[0039] FIG. 6A is a graph showing RGB values from the reference image, according to an aspect of the present disclosure;

[0040] FIG. 6B is a graph showing RGB values from the RandomForest model, according to an aspect of the present disclosure;

[0041] FIG. 7 is a graph showing a reconstruction of the spectrum of from the SPD sensor, according to an aspect of the present disclosure;

[0042] FIG. 8 is graph showing a spectrum reconstruction by modeling F1-F8 channels as Gaussians, according to an aspect of the present disclosure; Attorney Reference No. 444365.000020

[0043] FIG. 9 is a graph showing all six measurements scaled, according to an aspect of the present disclosure;

[0044] FIG. 10A is a chart showing RGB values after the SPD is reconstructed, according to an aspect of the present disclosure;

[0045] FIG. 10B is a chat showing RGB values of a reference, according to an aspect of the present disclosure;

[0046] FIG. 11 A is a diagram illustrating examples of this spectral method with a RandomF orest model applied to mobile camera images with almost simultaneous SPD readings, according to an aspect of the present disclosure;

[0047] FIG. 1 IB is a diagram illustrating examples of this spectral method with a RandomF orest model applied to mobile camera images with almost simultaneous SPD readings, according to an aspect of the present disclosure;

[0048] FIG 11C is a diagram illustrating examples of this spectral method with a RandomForest model applied to mobile camera images with almost simultaneous SPD readings, according to an aspect of the present disclosure;

[0049] FIG. 12A is a wireframe component diagram of a spectral sensing device, according to an aspect of the present disclosure;

[0050] FIG. 12B is a first perspective view of the spectral sensing device, according to an aspect of the present disclosure;

[0051] FIG. 12C is a bottom view of the spectral sensing device, according to an aspect of the present disclosure;

[0052] FIG. 12D is a second perspective view of the spectral sensing device, according to an aspect of the present disclosure;

[0053] FIG. 12E is a perspective view of internal components of the spectral sensing device, according to an aspect of the present disclosure;

[0054] FIG. 12F is a top view of the internal components of the spectral sensing device, according to an aspect of the present disclosure;

[0055] FIG. 13 is a component diagram of a machine in the example form of computer system, according to an aspect of the present disclosure;

[0056] FIG. 14 is a flowchart illustrating a method predicting microbiome composition from non- invasive multimodal data sources, according to an aspect of the present disclosure;

[0057] FIG. 15 is a flowchart of a method for generating a health risk assessment based on predicted microorganism abundance, according to an aspect of the present disclosure; and

[0058] FIG. 16 is a system diagram showing a model trained on SPD data that is deployable on RGB- only devices, according to an aspect of the present disclosure. Attorney Reference No. 444365.000020

[0059] The figures are for purposes of illustrating examples, but it is understood that the present disclosure is not limited to the arrangements and instrumentality shown in the drawings.

DETAILED DESCRIPTION

[0060] The following description sets forth exemplary aspects of the present disclosure. It should be recognized, however, that such description is not intended as a limitation on the scope of the present disclosure. Rather, the description also encompasses combinations and modifications to those exemplary aspects described herein.

[0061] The present disclosure relates to an advanced spectral sensing device for biometric diagnostics that incorporates multispectral, hyperspectral, and photoplethysmography (PPG) technologies. The device may be configured for use in various medical fields including integrative medicine, dentistry, dermatology, neurology, oncology, ophthalmology, cardiology, and psychology, among others, for diagnosing, monitoring, and preventing conditions based on features including tongue, oral cavity, saliva, teeth, nails, hair, scalp, wrist, breath, pulse, eye, retina, face, and reproductive organs.

[0062] System Architecture and Components

[0063] The spectral sensing device may leverage sensor technology to replicate functionalities of traditional multispectral and hyperspectral cameras and camera-based PPG sensors with reduced cost and complexity. The device may streamline the conventional process of multispectral and hyperspectral imaging through the use of a single-pixel detector for multispectral and hyperspectral tracking of the response of objects of interest. In some aspects, the device may be capable of capturing the spectral response of objects of interest at more than three distinct wavelengths.

[0064] The system may comprise a red-green-blue (RGB) color image sensor located at a first position for capturing a plurality of color images of one or more objects, where each of the plurality of color images has RGB values within the visible spectrum. A single pixel detector (SPD) spectroradiometer may be located at the first position to collect measurements for each of the plurality of color images, where the measurements comprise spectral values both inside and outside of the visible spectrum.

[0065] Integration with Smartphone Technology

[0066] The device may be designed to integrate seamlessly with existing camera technology found in smartphones. This integration may be achieved through algorithmic enhancements that enable a standard RGB camera on a smartphone to capture multispectral data effectively when used in conjunction with the spectral sensing device. This approach may reduce hardware costs while utilizing the widespread availability and sophisticated capabilities of modem smartphones.

[0067] Standard smartphone cameras may alter image properties through processes such as white balance adjustment and high dynamic range (HDR) imaging, which can skew diagnostic assessments. The device's technology may compensate for these alterations, providing standardized and accurate multispectral, hyperspectral and PPG data unaffected by the camera's internal processing. Attorney Reference No. 444365.000020

[0068] Near-Infrared Sensing Capabilities

[0069] The device may be equipped with one or more sensors capable of retrieving the Near-Infrared (NIR) channel between 850-950nm. These NIR sensors may be capable of performing blood flow monitoring and subcutaneous imaging up to a depth of 5mm, while also providing insights into hemoglobin absorption, blood glucose levels, water content, and melanin concentration. When applied to the stomach or heart area, the NIR sensor may measure gastric motility, fat and muscle composition, liver health, cardiac output, detection of tumors or abnormal growths, and respiratory function, providing comprehensive insights, prediction and prevention of various physiological conditions. Other suitable wavelength ranges within the visible, near-infrared, and ultraviolet spectra may be employed depending on the specific application and target analytes.

[0070] Machine Learning Implementation

[0071] The system may employ one or more Deep Learning (DL) models specifically tailored to process and analyze images captured by smartphone cameras. These models may be trained to identify and generate the spectral signatures that the device detects, adapting to the variable qualify and characteristics of images taken by consumer-grade cameras.

[0072] The DL architecture may be optimized for low-latency processing, ensuring that it can operate effectively within the computational constraints of standard smartphones. This may involve simplifying the model where possible while retaining sufficient complexify to accurately mimic the multispectral and hyperspectral data. The training process may include the application of advanced physical modeling, processes of regularization to prevent overfitting and the use of transfer learning to speed up the training process by utilizing pre-trained networks on similar tasks.

[0073] Data Collection and Training Process

[0074] Data collection may involve gathering thousands of images from volunteer participants, using a variety of smartphone models under different lighting conditions to ensure the robustness of the dataset. To ensure the model's effectiveness across diverse populations, special attention may be paid to capturing a wide range of demographic variables, such as age, gender, and ethnicity.

[0075] Several data augmentation strategies may be implemented to enhance the dataset. These may include geometric transformations and photometric adjustments to simulate different lighting conditions and camera angles, thereby enriching the training data without the need for additional image captures.

[0076] Each of the plurality of color images and respective measurements may be combined to form a training dataset. The training dataset may be used to train one or more machine models to associate the RGB values with the respective spectral values both inside and outside of the visible spectrum. The one or more machine models may be used to predict spectral values both inside and outside of the visible spectrum of one or more additional color images based on RGB values.

[0077] Color Reconstruction and Calibration Attorney Reference No. 444365.000020

[0078] The system may implement various methods for color reconstruction and calibration. A look-up table (LUT) 2nd-degree polynomial-based method may be used for image color correction. The system may collect data using a reference color checker, with both color camera and SPD sensor, and may collect multiple images of the color checker with an industrial-grade camera using various lighting conditions and camera settings.

[0079] FIG. 1A is a graph showing RGB values of original images. FIG. IB is a graph showing RBG values using the LUT 2nd-degree polynomial-based method for image color correction. Ideally, all measurements should fall on one diagonal ideal line. The measured correlation is 0.90 before the correction and 0.92 after. Also, the color shift may be corrected.

[0080] FIG. 2A is a chart showing RGB values of an original camera image. FIG. 2B is a chart showing RGB values in an image corrected by the LUT polynomial method. FIG. 2C shows a reference color checker.

[0081] In one example, by using only SPD measurements without an RGB camera, the system may develop a linear matrix method to predict color from SPD readings. This may be based on the idea that human perceived color can be represented by three values: R, G, and B. From the SPD sensor, the system may obtain multiple filter values and may use multiple narrow filter values (FL.Fn) from its channels. Alternative examples may employ different numbers of filters. In one approach, RGB values may be represented by a linear combination of SPD channels. For example, for the R channel:

[0082] R = a₀₀ + a₀₁Fl + ^02^2+. . . +a₀₈F8 Equation (1)

[0083] This way, the system may set up a matrix equation:

[0084] RGB = A x F Equation (2)

[0085] where RGB is a 1x3 vector of RGB values, A is an 8x3 matrix of coefficients, and F is a 1x8 vector of SPD channel readings (if using up to 8 filters F1..F8). The problem may be solved by estimating the matrix A using least squares and SPD measurements of the color checker table. Through this procedure, the system may be able to reconstruct the color checker image from only SPD sensor readings. FIG. 3A is a chart showing values for a reconstructed image. FIG. 3B is a chart showing a reference color checker.

[0086] The SPD sensor may provide more precise color measurement than a camera and may be more robust to lighting conditions. FIG. 4A is a graph showing RGB values from a camera with the color correction procedure. FIG. 4B is a graph showing RGB values from the SPD sensor. In validation studies, standard camera measurements with color correction achieved a correlation of 0.92 against a reference, which may improve to above 0.95 using the SPD sensor with a linear matrix method, and potentially approach 0.99 using a RandomForest model trained on the SPD sensor data. Alternatively, non-linear transformations may be employed. Attorney Reference No. 444365.000020

[0087] The system may achieve even better results using machine learning approaches. Various machine learning models including ensemble methods such as RandomForest model may be trained to provide improved correlation between reference and measured color values, potentially achieving correlation scores exceeding 0.95 in some implementations.

[0088] FIG. 5A is a chart showing RGB values from a reference image. FIG. 5B is a chart showing RGB values from the RandomForest model. FIG. 6A is a graph showing RGB values from the reference image. FIG. 6B is a graph showing RGB values from the RandomForest model. This demonstrates that the correlation between reference and measured color is almost perfect. The SPD sensor may provide more precise color measurement than a camera and may be more robust to lighting conditions, with correlation values that may improve significantly when comparing camera measurements with color correction to SPD sensor measurements.

[0089] Alternatively, non-linear transformations, polynomial regression, or other mathematical approaches may be employed to establish relationships between spectral measurements and color values.

[0090] Physical Modeling and Spectrum Reconstruction

[0091] A physical model of the SPD sensor may allow for the reconstruction of the spectrum of the object it records. This may be based on the SPD channels and information provided by the manufacturer. The system may model the spectral channels as Gaussians and perform spectrum reconstruction. FIG. 7 is a graph showing a reconstruction of the spectrum of from the SPD sensor. FIG. 8 is graph showing a spectrum reconstruction by modeling F1-F8 channels as Gaussians. This figure shows the spectrum of the black-gray-white fields (the first 6 fields) of the color checker. Each channel is color-coded, while the sum/reconstructed spectrum is black. FIG. 9 is a graph showing all six measurements scaled (because black-to-white should all be the same spectrum, just scaled in intensity).

[0092] To reconstruct color from the spectrum, a CIE color-matching function that represents human perception of color may be used. From the spectrum, the system may obtain XYZ values (tristimulus values) that may be converted to sRGB color space, taking into account gamma scaling. FIG. 10A is a chart showing RGB values after the SPD is reconstructed. FIG. 10B is a chat showing RGB values of a reference.

[0093] FIGs. 11A-11C are diagrams illustrating examples of this spectral method with a RandomForest model applied to mobile camera images with almost simultaneous SPD readings. FIGs. 11A-11C show spectral readings and RandomForest readings.

[0094] Multimodal Machine Learning System for Microbiome Analysis

[0095] The system may incorporate a comprehensive multimodal machine learning architecture designed to predict microbiome composition from non-invasive daily scans. This system may combine Attorney Reference No. 444365.000020 spectral imaging data with conventional photography and user-reported information to generate quantitative predictions of microbial abundance in biological samples.

[0096] The data processing pipeline may utilize established bioinformatics protocols to establish ground truth microbial composition data. Biological samples may be processed using metagenomic sequencing protocols, with taxonomic classification performed using tools such as Kraken2, MetaPhlAn, or similar bioinformatics platforms to determine species-level abundance for each sample. Alternative taxonomic classification tools and bioinformatics pipelines may be employed for microbial identification and abundance quantification. The resulting microbial abundance profiles may serve as ground truth labels for training machine learning models.

[0097] The training dataset may comprise paired measurements collected within a specified time window, such as approximately 15-45 days from ground-truth sample dates. The multimodal data may include spectral images captured as single-channel images by the proprietary spectral device, conventional RGB photographs of anatomical features such as tongue surfaces, and user-reported data including demographic information, lifestyle factors, and daily questionnaire responses.

[0098] The multimodal architecture may comprise three parallel input branches, each optimized for processing different data modalities. The first branch may utilize a convolutional neural network configured to process spectral images, such as 64x64, 128x128, 256x256 pixel or other suitable dimension single-channel images. The second branch may employ a pre-trained ResNet model, such as ResNetl8, that may be fine-tuned specifically for processing conventional photographs. The third branch may comprise a feed-forward network, such as a multi-layer perceptron, configured to process tabular user data. In some examples, the tabular user data branch is omitted, and predictions are based on only spectral images and/or conventional photographs.

[0099] In one non-limiting example, the multimodal neural network may be implemented using deep learning frameworks such as Py Torch, Tensor Flow, or similar platforms. The spectral encoder may comprise one or more convolutional layers with configurable filter counts and kernel sizes, followed by activation functions and pooling operations as appropriate for the specific implementation. The photographic encoder may utilize pre-trained convolutional neural networks such as ResNet architectures, VGG networks, or other suitable models, with output layers configured to generate feature vectors of appropriate dimensionality. The tabular encoder may comprise multi-layer perceptrons or other feed-forward architectures with one or more hidden layers sized according to the input data characteristics and computational requirements. The final predictor head may process concatenated features through one or more fully connected layers, optionally incorporating regularization techniques such as dropout, batch normalization, or other methods to improve generalization performance.

[0100] The system may implement feature fusion by concatenating outputs from the three parallel processing branches into a combined feature vector. This combined representation may be processed Attorney Reference No. 444365.000020 through one or more prediction heads configured to output predicted abundance values for target bacteria and other microorganisms.

[0101] Target Microorganisms and Clinical Applications

[0102] The system may be configured to predict the abundance of clinically relevant microorganisms including bacteria, fungi, and viruses present in biological samples. Target organisms may include Streptococcus salivarius, Streptococcus parasanguinis, Porphyromonas gingivalis, Fusobacterium nucleatum, and other species associated with specific medical conditions.

[0103] The system may be configured to train separate models for different target bacteria, enabling specialized prediction capabilities for each organism of interest. The system may achieve high prediction accuracy with Mean Absolute Error values of approximately 0. 15 or less in some implementations, and correlation coefficients that may exceed 0.95 in various examples. In some examples, performance validation may demonstrate the system's capability to achieve high accuracy in microbiome prediction tasks, with the system potentially achieving a Mean Absolute Error of 0.12 or less for Streptococcus salivarius prediction in some implementations.

[0104] The predicted microbial abundances may be utilized to generate risk assessments for various medical conditions. For example, Streptococcus salivarius levels may be associated with Crohn's disease risk assessment, Streptococcus parasanguinis abundance may correlate with colorectal cancer or periodontal disease risk evaluation, and Porphyromonas gingivalis presence may indicate elevated Alzheimer's disease risk.

[0105] Specific Bacterial Predictions and Disease Associations

[0106] In some examples, the system may be configured to predict specific bacterial abundances that may correlate with particular medical conditions. The system may also be used for prediction of the whole oral microbiome, virome, and mycobiome, as well as for stool microbiome analysis. For Streptococcus salivarius detection, the system may achieve prediction accuracies exceeding 98% with MAE values of approximately 0.15 or less in some cases, which may enable assessment of Crohn's disease risk based on associations between reduced S. salivarius levels and inflammatory bowel conditions. The spectral signatures associated with this organism may be characterized by specific absorption patterns in the 550-650nm range, which may correspond to metabolic byproducts and cellular components.

[0107] In some aspects, Streptococcus parasanguinis abundance predictions may be utilized for colorectal cancer and periodontal disease risk assessment, with the system achieving MAE values of approximately 0.26 for this target organism in some implementations. The spectral characteristics of S. parasanguinis may include features in the near-infrared region around 700-800nm, which may reflect cellular wall compositions and metabolic signatures. Attorney Reference No. 444365.000020

[0108] Porphyromonas gingivalis detection may serve as an indicator for Alzheimer's disease risk in some examples, with the system configured to identify spectral patterns that may be associated with this anaerobic bacterium. The organism's black pigmentation from heme compounds may produce absorption signatures in the 400-5 OOnm range, which may enable identification through spectral analysis.

[0109] Additional target organisms may include Fusobacterium nucleatum for cardiovascular disease risk assessment, Prevotella intermedia for diabetes monitoring, and Aggregatibacter actinomycetemcomitans for aggressive periodontitis detection in some implementations. Each organism may exhibit spectral fingerprints based on their metabolic products, cellular structures, and pigmentation characteristics. In some examples, the system may analyze these spectral signatures to provide comprehensive assessment of oral and stool microbiome, virome, and mycobiome compositions.

[0110] Periodontitis Monitoring and Inflammation Detection

[oni] The system may be configured to monitor and predict periodontitis progression through multispectral analysis of inflammatory signatures in oral tissues. Periodontal inflammation may be characterized by specific spectral changes that reflect increased blood flow, tissue edema, and altered cellular metabolism in affected gingival tissues.

[0112] The multispectral signature of periodontal inflammation may include increased absorption in the 540-580nm range corresponding to elevated hemoglobin concentrations from increased vascularization and blood pooling in inflamed tissues. Near-infrared spectral features around 760-900nm may indicate changes in tissue oxygenation and water content associated with inflammatory edema and altered perfusion patterns. The system may utilize various wavelength ranges for different physiological assessments, including but not limited to near-infrared channels (such as those around 900-920nm), visible spectrum ranges for hemoglobin detection (such as 530-590nm), and other spectral regions for tissue analysis. The specific wavelength ranges may be selected based on the target analytes and measurement requirements.

[0113] Inflammatory biomarkers may be detected through spectral analysis of specific wavelength regions that correspond to cytokine activity and immune cell infiltration. The 600-650nm range may show characteristic absorption patterns related to inflammatory mediators such as prostaglandins and interleukins, while the 700-75 Onm region may reflect changes in tissue structure and collagen degradation associated with periodontal breakdown. The system may utilize various wavelength ranges for different physiological assessments, including but not limited to near-infrared channels (such as those around 900-920nm), visible spectrum ranges for hemoglobin detection (such as 530-590nm), and other spectral regions for tissue analysis. The specific wavelength ranges may be selected based on the target analytes and measurement requirements Attorney Reference No. 444365.000020

[0114] The system may implement machine learning models trained on longitudinal datasets correlating multispectral measurements with clinical periodontal parameters including probing depths, bleeding on probing, and clinical attachment levels. These models may enable early detection of periodontal inflammation before clinical symptoms become apparent, facilitating preventive interventions and treatment monitoring.

[0115] Temporal analysis of spectral signatures may allow for tracking of disease progression and treatment response over time. The system may establish baseline spectral profiles for individual patients and monitor deviations that indicate inflammatory activity or healing responses following periodontal therapy.

[0116] Integration with existing periodontal assessment protocols may enhance diagnostic accuracy by providing objective, quantitative measurements to supplement traditional clinical examination methods. The non-invasive nature of spectral analysis may enable more frequent monitoring without patient discomfort, supporting improved periodontal health management and early intervention strategies.

[0117] Validation and Performance Metrics

[0118] The system may be configured to perform comprehensive validation procedures, including userholdout validation where models are tested on entirely unseen users to assess generalization capabilities across different populations. This validation approach may ensure that the system maintains accuracy when deployed to new users who were not included in the training dataset.

[0119] Performance evaluation may utilize various metrics including Mean Absolute Error, correlation coefficients, and R-squared values comparing predicted abundances to ground truth sequencing results. The system may achieve high correlation values, potentially exceeding 0.99 in some implementations, demonstrating strong agreement between predicted and actual microbial abundances.

[0120] Cross-validation techniques may be employed to assess model robustness across different demographic groups, clinical sites, and measurement conditions. The validation process may include assessment of the system's performance across diverse populations to ensure equitable accuracy across different user groups.

[0121] To validate the inventive contribution of the multimodal approach, an ablation study was performed. The results demonstrated that the full multimodal model (MAE 1.196, R-squared 0.590) significantly outperformed models where data was removed. Notably, removing the spectral image data resulted in a substantial degradation of performance (MAE 2.082, R-squared 0. 123), confirming that the fusion of spectral data with other modalities is a critical and non-obvious component of the system's accuracy.

[0122] Device-Free Implementation and Accessibility

[0123] The system may be implemented in a device-free configuration that utilizes standard smartphone cameras in conjunction with trained machine learning models. This approach may enable widespread Attorney Reference No. 444365.000020 deployment of microbiome analysis capabilities without requiring specialized hardware, while maintaining diagnostic accuracy through the spectral reconstruction methods.

[0124] The device-free implementation may leverage the machine learning models trained on paired RGB and spectral data to predict spectral characteristics from standard smartphone camera images. This may significantly reduce the barrier to adoption by eliminating the need for specialized spectral sensing hardware while preserving the analytical capabilities of the system.

[0125] The near-infrared reconstruction capability may achieve moderate to strong correlations between predicted and reference measurements, with validation experiments showing correlation coefficients exceeding 0.94 for certain biological objects (e.g., banana) and 0.87 for synthetic objects (e.g., marker) [0126] Clinical Integration and Monitoring Applications

[0127] The system may be integrated into clinical workflows for continuous health monitoring, early disease detection, and treatment response assessment. The non-invasive nature of the imaging approach combined with quantitative microbiome predictions may enable regular monitoring of microbial composition changes over time, supporting personalized medicine approaches and preventive healthcare strategies.

[0128] Healthcare providers may utilize the system to track treatment efficacy through before-and-after comparisons of microbial profiles, enabling objective evaluation of therapeutic interventions. The system may quantify treatment response through standardized metrics such as microbial diversity indices, pathogen-to-commensal ratios, or disease-specific risk scores.

[0129] The system may enable personalized treatment protocols by providing individual-specific microbiome profiles and health risk assessments. Baseline measurements may be established for each patient, creating personalized reference profiles that account for individual variations in anatomy, physiology, and native microbiome characteristics.

[0130] Microbiome Profiling Architecture

[0131] The prediction method may incorporate compensation mechanisms that account for smartphone camera processing effects that can alter the spectral characteristics of captured images. White balance adjustments applied by smartphone cameras may shift the color temperature of images to compensate for different illumination sources, potentially affecting the accuracy of spectral predictions. The trained models may include correction factors that reverse or compensate for these white balance modifications to restore the original spectral relationships present in the captured scenes.

[0132] High dynamic range (HDR) processing implemented by smartphone cameras may combine multiple exposures to extend the dynamic range of captured images. This HDR processing may introduce non-linear transformations that alter the relationship between scene radiance and recorded pixel values. The prediction models may incorporate inverse transformations that account for HDR processing effects, enabling accurate spectral reconstruction from HDR-processed images. Attorney Reference No. 444365.000020

[0133] The spectral resolution enhancement achieved through the prediction method may extend the effective spectral capabilities of standard RGB cameras from three broad-band channels to multiple distinct spectral channels with enhanced wavelength specificity. The narrow-band channel predictions may provide spectral resolution improvements that approach those of dedicated multispectral imaging systems while utilizing widely available RGB camera hardware.

[0134] The prediction process may operate through forward inference procedures where RGB pixel values serve as input features to the trained neural network models. The network architectures may process spatial patterns within RGB images to extract spectral information that corresponds to the extended wavelength ranges captured during the training phase. The inference process may generate output predictions for each of the multiple spectral channels simultaneously, producing complete multispectral reconstructions from single RGB input images.

[0135] The computational requirements for the prediction method may be optimized for deployment on mobile devices and embedded systems with limited processing capabilities. The neural network architectures may be designed with reduced parameter counts and computational complexity to enable real-time or near-real-time spectral reconstruction on smartphone processors. Model quantization and pruning techniques may be applied to further reduce the computational overhead while maintaining prediction accuracy.

[0136] The prediction method may support batch processing capabilities that enable the simultaneous reconstruction of multiple images or video sequences. Temporal consistency constraints may be applied when processing video data to ensure that spectral predictions remain stable across consecutive frames. The batch processing approach may improve computational efficiency by leveraging parallel processing capabilities available in modem mobile processors and graphics processing units.

[0137] Quality assessment metrics may be integrated into the prediction method to evaluate the reliability of spectral reconstructions for individual images or image regions. These quality metrics may identify areas within images where prediction confidence is reduced due to challenging imaging conditions, unusual spectral characteristics, or limitations in the training data coverage. The qualify assessment information may be provided to users or downstream analysis systems to guide the interpretation of spectral reconstruction results.

[0138] The prediction method may incorporate adaptive processing capabilities that adjust the reconstruction parameters based on detected imaging conditions or target characteristics. Scene classification algorithms may identify the types of materials or biological tissues present in input images, enabling the selection of specialized model parameters or correction factors that are optimized for specific target categories. This adaptive approach may improve prediction accuracy across diverse application scenarios and imaging environments. Attorney Reference No. 444365.000020

[0139] The multimodal data integration and processing pipeline may encompass a comprehensive framework that combines spectral imaging data, conventional RGB photographs, and tabular user information into unified datasets suitable for machine learning model training and biological parameter prediction. The pipeline architecture may integrate multiple data sources through systematic aggregation procedures that maintain temporal and spatial relationships between different measurement modalities. [0140] The bioinformatics processing component of the pipeline may begin with raw DNA sequencing data in FASTQ format (.fq.gz files) obtained from biological samples such as tongue swabs or other tissue specimens. The FASTQ files may contain paired-end sequencing reads that represent the genetic material present in the collected samples. The bioinformatics workflow may process these raw sequencing files through multiple computational stages to generate quantitative bacterial abundance profiles that serve as ground truth data for model training.

[0141] The demultiplexing process may separate pooled sequencing reads based on sample-specific barcode sequences that identify the origin of each sequencing fragment. Cutadapt software may be employed to perform the demultiplexing operations, extracting reads that correspond to individual sample identifiers from the combined sequencing datasets. The demultiplexing procedure may ensure that sequencing reads are correctly assigned to their respective sample sources, enabling accurate downstream analysis of sample-specific microbial compositions.

[0142] Qualify control procedures may be applied to the demultiplexed sequencing reads using qualify control software such as Fastp to remove low-quality sequences and adapter contamination that could interfere with taxonomic classification accuracy. The qualify trimming process may eliminate sequencing reads with poor base qualify scores, excessive ambiguous nucleotides, or insufficient length for reliable taxonomic assignment. The quality control steps may improve the reliability of subsequent taxonomic classification by ensuring that only high-quality sequencing data proceeds through the analysis pipeline.

[0143] Taxonomic classification software such as Kraken2, MetaPhlAn, or similar bioinformatics platforms may process the quality-controlled sequencing reads to identify the bacterial species present in each sample and quantify their relative abundances. Taxonomic classification tools such as Kraken2, MetaPhlAn, or similar bioinformatics platforms algorithm may compare sequencing reads against comprehensive reference databases containing known bacterial genome sequences to determine the most likely taxonomic assignments for each read. The classification process may generate species-level abundance estimates that represent the relative proportions of different bacterial taxa within each sample.

[0144] The taxonomic classification results may be compiled into feature tables where rows correspond to individual sample identifiers and columns represent the relative abundance values for identified bacterial species. Each entry in the feature table may indicate the percentage or fractional abundance of Attorney Reference No. 444365.000020 a specific bacterial species within a particular sample. The feature tables may serve as ground truth datasets that provide quantitative microbiome composition profiles for correlation with spectral imaging measurements.

[0145] The Firebase database integration component may facilitate the collection and storage of timeseries data from mobile applications and connected devices. The Firebase platform may provide cloudbased data storage capabilities that enable real-time synchronization of user-generated data across multiple devices and platforms. The database architecture may support the storage of diverse data types, including image files, numerical measurements, and structured questionnaire responses.

[0146] Daily scan data collection may occur through mobile applications that capture spectral images, conventional tongue photographs, and user-reported information on a regular basis. The spectral images may be generated using the device in conjunction with smartphone cameras to produce multispectral datasets that correspond to specific measurement dates and times. The conventional photographs may provide complementary visual information that captures spatial details and color characteristics not fully represented in the spectral measurements.

[0147] The tabular user data component may include both static demographic information and dynamic responses to daily questionnaires administered through the mobile application interface. Static data elements may encompass user characteristics such as height, weight, age, gender, and lifestyle factors including smoking habits, dietary preferences, and medical history information. Dynamic questionnaire data may capture daily variations in user behavior and physiological status, including recent food consumption patterns, symptom reports, oral hygiene practices, and medication usage. In some examples, the tabular user data branch is omitted, and predictions are based on only spectral images and/or conventional photographs.

[0148] The data aggregation process may combine ground truth microbiome profiles with corresponding daily scan measurements through temporal matching procedures that identify scan data collected within specified time windows relative to biological sample collection dates. The temporal aggregation approach may define time intervals, such as ±30 days, around each ground truth measurement to capture daily scans that correspond to similar microbiome states.

[0149] The time window selection may account for the temporal stability of microbiome compositions and the practical constraints of biological sample collection frequencies. Microbiome profiles may exhibit relative stability over periods of weeks to months, enabling the association of multiple daily scans with single ground truth measurements. The temporal aggregation strategy may expand the effective size of training datasets by creating multiple training instances from each biological sample.

[0150] The aggregation procedure may query the Firebase database to retrieve all daily scan records that fall within the defined time windows for each user's ground truth measurements. The database queries may filter scan data based on user identifiers, measurement dates, and data completeness criteria Attorney Reference No. 444365.000020 to ensure that only valid scan records are included in the aggregated datasets. The filtering process may exclude incomplete scans or measurements with technical issues that could compromise model training quality.

[0151] The master multimodal table generation may combine the aggregated scan data with corresponding ground truth microbiome profiles to create comprehensive training datasets. Each row in the master table may represent a single training instance that includes spectral image file paths, conventional photograph storage locations, tabular data values, and target bacterial abundance values from the taxonomic classification results. The table structure may facilitate efficient data loading and processing during machine learning model training procedures.

[0152] The data preprocessing pipeline may apply standardization and normalization procedures to ensure consistency across different data modalities and measurement sessions. Spectral image preprocessing may include intensity normalization, spatial registration, and artifact removal procedures that standardize the spectral measurements across different imaging conditions. Conventional photograph preprocessing may involve color correction, resolution standardization, and quality assessment procedures that ensure consistent image characteristics.

[0153] Tabular data preprocessing may include missing value imputation, categorical variable encoding, and numerical feature scaling procedures that prepare the user-reported information for integration with image-based features. The preprocessing steps may address variations in data collection procedures, user response patterns, and measurement device characteristics that could introduce systematic biases into the training datasets.

[0154] The pipeline architecture may support parallel processing capabilities that enable efficient handling of large datasets containing thousands of images and associated measurements. Distributed computing frameworks may be employed to accelerate data processing operations, particularly for computationally intensive procedures such as image preprocessing and feature extraction. The parallel processing approach may reduce the time required for dataset preparation and model training procedures.

[0155] Data validation procedures may be implemented throughout the pipeline to ensure the integrity and consistency of aggregated datasets. Cross-referencing checks may verify that spectral measurements, photographs, and tabular data correspond to the same users and measurement sessions. Temporal validation may confirm that daily scan data falls within the specified time windows relative to ground truth sample collection dates.

[0156] The pipeline may incorporate version control and data provenance tracking capabilities that maintain records of data processing steps and parameter settings used during dataset generation. The provenance information may enable reproducible dataset creation and facilitate the identification of processing steps that contribute to model performance variations. Version control may support iterative Attorney Reference No. 444365.000020 dataset refinement and the comparison of model performance across different data processing configurations.

[0157] The multimodal neural network architecture may comprise three distinct processing branches that operate in parallel to extract features from different data modalities before combining the extracted information for final bacterial abundance predictions. The three-branch design may enable the simultaneous processing of spectral imaging data, conventional photographic information, and tabular user data through specialized neural network components that are optimized for each respective data type.

[0158] The spectral image processing branch may comprise a convolutional neural network architecture specifically designed to analyze spectral images of various resolutions, such as 64x64, 128x128 to 256x256 pixels or other suitable dimensions. The spectral branch may begin with a convolutional layer that applies a plurality of filters with kernel dimensions ranging from 3x3 to 7x7, such as 32 filters with 3x3 kernels in one implementation to the single-channel spectral input images. The convolutional operation may extract spatial features that correspond to spectral patterns and intensity variations across the spectral image data. A rectified linear unit (ReLU) activation function may be applied after the first convolutional layer to introduce non-linearity into the feature extraction process.

[0159] The spectral processing branch may incorporate a max pooling layer with 2x2 pooling windows that reduces the spatial dimensions of the feature maps while preserving the most prominent spectral features. The pooling operation may downsample the feature representations from 128x128 to 64x64 spatial dimensions, reducing computational requirements for subsequent processing stages. A second convolutional layer may apply 64 filters with 3x3 kernels to the pooled feature maps, further extracting hierarchical spectral patterns at reduced spatial resolution.

[0160] The second convolutional layer in the spectral branch may be followed by another ReLU activation function and a second max pooling operation that further reduces the spatial dimensions to 32x32 pixels. The cascaded convolutional and pooling operations may create a hierarchical feature extraction process that captures spectral patterns at multiple spatial scales within the input images. A flattening operation may convert the final convolutional feature maps into a one-dimensional vector suitable for concatenation with features from other network branches.

[0161] The photographic image processing branch may utilize a pre-trained ResNetl8 architecture that has been adapted for processing conventional RGB tongue photographs. The ResNetl8 model may leverage residual connections and batch normalization techniques that facilitate the training of deeper network architectures while maintaining gradient flow throughout the network depth. The pre-trained weights may provide initial feature representations that have been learned from large-scale image datasets, enabling transfer learning capabilities for tongue-specific image analysis. Attorney Reference No. 444365.000020

[0162] The ResNetl8 architecture in the photographic branch may process RGB images through multiple residual blocks that combine convolutional operations with skip connections. Each residual block may apply batch normalization and ReLU activation functions to intermediate feature representations, maintaining stable training dynamics throughout the network depth. The skip connections may enable the direct propagation of input features to deeper layers, allowing the network to learn incremental feature transformations rather than complete input-to-output mappings.

[0163] The final fully connected layer of the original ResNetl8 architecture may be replaced with a custom linear layer that outputs 128-dimensional feature vectors. This modification may adapt the pretrained network for the specific requirements of tongue photograph analysis while maintaining the learned feature extraction capabilities from the earlier network layers. The 128-dimensional output may provide a compact representation of photographic features that can be efficiently combined with features from other network branches.

[0164] The tabular data processing branch may comprise a feed-forward multi-layer perceptron (MLP) architecture designed to process numerical and categorical user information. The tabular branch may accept input vectors containing user demographic data, lifestyle information, and daily questionnaire responses that have been preprocessed through normalization and encoding procedures. The input dimensionality may vary based on the number of tabular features included in the dataset, with the network architecture adapting to accommodate different feature set sizes.

[0165] The first layer of the tabular processing branch may comprise a linear transformation that maps the input tabular features to a hidden representation with dimensions ranging from approximately 32 to 128, such as a 64-dimensional representation in one implementation. The linear layer may apply learned weight matrices and bias terms to transform the input features into an intermediate feature space that captures relevant patterns within the user data. A ReLU activation function may be applied after the first linear transformation to introduce non-linearity into the tabular feature processing.

[0166] The tabular branch may include a second linear layer that further transforms the 64-dimensional hidden representation into an output vectors with dimensions ranging from approximately 32 to 512, such as 128-dimensional, 32-dimensional, and 512-dimensional vectors in particular examples. This second transformation may provide additional capacity for learning complex relationships within the tabular data while maintaining computational efficiency. The output feature vectors with dimensions ranging from approximately 32 to 512, such as 128-dimensional, 32-dimensional, and 512-dimensional vectors in particular examples may serve as the final feature representation from the tabular processing branch for combination with features from the spectral and photographic branches.

[0167] The feature concatenation process may combine the output vectors from all three processing branches into a unified feature representation. The spectral branch may contribute a flattened feature vector with dimensions determined by the final convolutional layer output size, calculated as 64 Attorney Reference No. 444365.000020 channels in particular embodiments multiplied by the spatial dimensions after pooling operations. The photographic branch may contribute a feature vectors with dimensions ranging from approximately 32 to 512, such as 128 -dimensional, 32-dimensional, and 512-dimensional vectors in particular examples from the modified ResNetl8 output layer. The tabular branch may contribute feature vectors with dimensions ranging from approximately 32 to 512, such as 128-dimensional, 32-dimensional, and 512- dimensional vectors in particular examples from the final MLP layer.

[0168] The concatenated feature vector may be fed into a final prediction head comprising multiple fully connected layers that map the combined multimodal features to bacterial abundance predictions. The first layer of the prediction head may apply a linear transformation that maps the concatenated features to a hidden representation feature vectors with dimensions ranging from approximately 32 to 512, such as 128-dimensional, 32-dimensional, and 512-dimensional vectors in particular examples. This expansion may provide increased capacity for learning complex interactions between features from different data modalities.

[0169] The prediction head may incorporate dropout regularization with a dropout probability of dropout regularization with probabilities between approximately 0.1 and 0.5, such as 0.3 and 0.2 in specific implementations applied after the first fully connected layer. The dropout operation may randomly set a fraction of the hidden unit activations to zero during training, preventing overfitting and improving generalization performance on unseen data. The dropout regularization may be particularly valuable when training on limited datasets where overfitting risks are elevated.

[0170] A second fully connected layer in the prediction head may transform the hidden representation of feature vectors with dimensions ranging from approximately 32 to 512, such as 128-dimensional, 32- dimensional, and 512-dimensional vectors in particular examples to an intermediate representation of feature vectors with dimensions ranging from approximately 32 to 512, such as 128-dimensional, 32- dimensional, and 512-dimensional vectors in particular examples. This layer may be followed by another ReLU activation function and a second dropout operation with a dropout regularization with probabilities between approximately 0.1 and 0.5, such as 0.3 and 0.2 in specific implementations. The cascaded fully connected layers may provide multiple stages of feature transformation that enable the learning of complex non-linear relationships between multimodal input features and target bacterial abundance values.

[0171] The final output layer of the prediction head may comprise a single linear unit that produces continuous scalar values representing predicted bacterial abundance levels. The output layer may not include activation functions, allowing the network to produce unbounded continuous predictions that can represent the full range of bacterial abundance values present in the training data. The continuous output format may enable the network to perform regression tasks for predicting quantitative bacterial abundance measurements. Attorney Reference No. 444365.000020

[0172] The training process for the multimodal architecture may employ adaptive optimization algorithms such as Adam, AdamW, or similar gradient-based optimizers, which combines the adaptive learning rate capabilities of the Adam optimizer with weight decay regularization. The adaptive optimization algorithms such as Adam, AdamW, or similar gradient-based optimizers may maintain separate learning rate schedules for different network parameters while applying L2 regularization to prevent overfitting. The optimizer configuration may include learning rate scheduling that reduces the learning rate during training to improve convergence stability.

[0173] The loss function for training the multimodal network may utilize Mean Absolute Error (LI loss), which computes the average absolute differences between predicted and target bacterial abundance values. The LI loss function may provide robust training dynamics for regression tasks and may be less sensitive to outliers compared to squared error loss functions. The LI loss may be particularly suitable for bacterial abundance prediction tasks where the target values may exhibit varying scales across different bacterial species.

[0174] The network architecture may support separate model training for different target bacterial species, enabling specialized prediction models for individual bacterial taxa. Each species-specific model may be trained using the same multimodal architecture but with target labels corresponding to the abundance values for the specific bacterial species of interest. This approach may enable the optimization of model parameters for individual bacterial species while maintaining consistent network architectures across different prediction tasks.

[0175] The validation process for the multimodal architecture may employ user-based data partitioning strategies where complete user datasets are reserved for validation testing. This partitioning approach may ensure that model evaluation reflects true generalization capabilities across different individuals rather than temporal variations within the same users. The user-based validation may provide more realistic assessments of model performance for deployment scenarios where predictions are made for previously unseen users.

[0176] The microbiome composition prediction methods may encompass computational approaches for determining the relative abundance of specific oral bacterial species through analysis of multimodal data inputs. The prediction methods may focus on bacterial taxa that exhibit clinical relevance for health monitoring applications, including Streptococcus salivarius, Streptococcus parasanguinis, Rothia mucilaginosa, and Porphyromonas gingivalis. Each bacterial species may be addressed through dedicated prediction models that are trained to recognize spectral and imaging patterns associated with the presence and abundance levels of the target organisms.

[0177] The bacterial species selection process may prioritize organisms that demonstrate established correlations with health conditions or physiological states. Streptococcus salivarius may be selected based on documented associations with inflammatory bowel conditions and oral lesion development. Attorney Reference No. 444365.000020

Streptococcus parasanguinis may be included due to correlations with colorectal cancer risk factors and periodontal disease progression. Rothia mucilaginosa may be targeted for prediction based on associations with oral cavity health indicators. Porphyromonas gingivalis may be selected due to documented relationships with neurodegenerative conditions and cognitive decline markers.

[0178] The separate model training approach may involve the development of individual neural network architectures for each target bacterial species rather than attempting to predict multiple species simultaneously through a single model. Each species-specific model may utilize the same multimodal neural network architecture but may be trained with target labels corresponding to the relative abundance values for the specific bacterial organism of interest. The separate training approach may enable the optimization of model parameters and feature representations that are tailored to the spectral and imaging characteristics associated with individual bacterial species.

[0179] The training datasets for each bacterial species model may be derived from the same multimodal data collection procedures but may be labeled with different target abundance values extracted from the taxonomic classification results. The ground truth abundance values for each species may be obtained from the Kraken2 taxonomic classification pipeline applied to tongue swab sequencing data. The abundance values may be expressed as relative percentages or fractional representations that indicate the proportion of each bacterial species within the total microbial community composition.

[0180] The model training process for each bacterial species may employ identical neural network architectures and training procedures to ensure consistency across different prediction tasks. The three- branch multimodal architecture may process spectral images, conventional photographs, and tabular user data through the same network components for all bacterial species models. The training parameters, including learning rates, batch sizes, and regularization settings, may be maintained consistently across different species-specific models to enable fair comparisons of prediction performance.

[0181] The user-holdout validation approach may provide rigorous evaluation procedures that assess model generalization capabilities across previously unseen individuals. The validation methodology may involve the complete removal of one or more users from the training dataset, reserving all data associated with the holdout users for final model evaluation. The holdout approach may ensure that model performance assessments reflect true generalization across different individuals rather than temporal variations within the same users.

[0182] The dataset partitioning process for user-holdout validation may randomly select specific users for holdout testing while ensuring that the remaining users provide sufficient training data for model development. The selection process may account for the distribution of bacterial abundance values across different users to ensure that the holdout validation set contains representative examples of the Attorney Reference No. 444365.000020 target bacterial species. The partitioning may maintain temporal relationships within individual user datasets while preventing data leakage between training and validation sets.

[0183] The training phase for each bacterial species model may utilize data from all users except those designated for holdout validation. The training process may proceed through multiple epochs using the AdamW optimizer and Mean Absolute Error loss function until convergence criteria are satisfied. The model parameters that achieve the lowest validation loss during training may be preserved for final evaluation on the holdout test set.

[0184] The evaluation process for holdout validation may involve loading the trained models and applying the models to all available daily scan data from the holdout users. The evaluation procedure may generate time-series predictions that estimate bacterial abundance levels for each daily scan collected from the holdout users. The predicted abundance values may be compared against the ground truth measurements obtained from tongue swab sequencing for the holdout users.

[0185] The Mean Absolute Error calculation for model evaluation may compute the average absolute differences between predicted bacterial abundance values and the corresponding ground truth measurements from sequencing analysis. The MAE metric may provide a direct measure of prediction accuracy that is expressed in the same units as the target abundance values. MAE values of 0. 12 or lower may indicate high prediction accuracy, corresponding to average prediction errors of 0.12 percentage points or less for bacterial abundance estimates.

[0186] The achievement of Mean Absolute Error values of 0. 12 or less may be demonstrated for specific bacterial species predictions, particularly for Streptococcus salivarius abundance estimation. The low MAE values may correspond to prediction accuracy levels exceeding 98% when calculated as the complement of the relative error with respect to the typical abundance ranges observed for the target bacterial species. The high accuracy levels may indicate that the multimodal prediction approach can reliably estimate bacterial abundance levels from non-invasive imaging and user data inputs.

[0187] The accuracy calculation methodology may account for the typical abundance ranges observed for different bacterial species within the oral microbiome. Species that exhibit higher baseline abundance levels may demonstrate different accuracy characteristics compared to species with lower typical abundance values. The accuracy metrics may be normalized relative to the expected abundance ranges for each species to provide meaningful comparisons across different bacterial taxa.

[0188] The time-series prediction capability may enable the generation of longitudinal bacterial abundance estimates that track changes in microbial composition over extended time periods. The daily scan data from holdout users may provide multiple prediction opportunities that span weeks or months of data collection. The time-series predictions may reveal temporal patterns in bacterial abundance that correspond to lifestyle changes, dietary modifications, or health status variations. Attorney Reference No. 444365.000020

[0189] The validation results may demonstrate consistent prediction performance across different holdout users, indicating that the trained models can generalize effectively to previously unseen individuals. The cross-user generalization capability may be particularly valuable for deployment scenarios where models are applied to new users who were not included in the original training datasets. The robust generalization performance may support the practical application of the prediction methods for continuous microbiome monitoring applications.

[0190] The prediction confidence assessment may provide additional information about the reliability of individual bacterial abundance estimates. The confidence metrics may be derived from model uncertainty quantification techniques or ensemble prediction approaches that generate multiple estimates for each input sample. The confidence information may guide users and healthcare providers in interpreting prediction results and making informed decisions based on the estimated bacterial abundance levels.

[0191] The comparative performance analysis across different bacterial species may reveal variations in prediction accuracy that correspond to the spectral and imaging characteristics associated with different organisms. Some bacterial species may exhibit more distinctive spectral signatures or visual patterns that facilitate accurate prediction, while other species may present greater challenges for non- invasive detection. The performance variations may inform the selection of bacterial targets for clinical applications and guide future model development efforts.

[0192] The scalability of the separate model training approach may enable the extension of prediction capabilities to additional bacterial species as training data becomes available. New bacterial targets may be incorporated through the training of additional species-specific models using the same multimodal architecture and validation procedures. The modular approach may facilitate the continuous expansion of bacterial prediction capabilities without requiring modifications to existing trained models.

[0193] The medical diagnostic applications enabled by the microbiome prediction system may encompass a comprehensive range of health monitoring and disease detection capabilities across multiple medical specialties. The system may provide continuous assessment of bacterial abundance levels that correlate with various pathological conditions, enabling early detection and monitoring of disease progression through non-invasive spectral analysis methods.

[0194] The Streptococcus salivarius prediction capabilities may facilitate monitoring of inflammatory bowel conditions, particularly Crohn's disease, where elevated abundance levels of this bacterial species have been documented in clinical studies. Patients with Crohn's disease may exhibit elevated relative abundance of Streptococcus salivarius in oral cavity samples, particularly when oral lesions are present. The spectral sensing system may detect these elevated abundance levels through daily monitoring procedures, providing continuous assessment of disease status without requiring invasive tissue sampling or laboratory-based microbiome analysis. Attorney Reference No. 444365.000020

[0195] The correlation between Streptococcus salivarius abundance and oral lesion development may enable the system to provide early warning indicators for inflammatory bowel disease exacerbations. The continuous monitoring capability may track changes in bacterial abundance levels over time, identifying trends that precede clinical symptom manifestation. Healthcare providers may utilize these abundance trend data to adjust therapeutic interventions or modify treatment protocols before acute inflammatory episodes occur.

[0196] The Streptococcus parasanguinis detection capabilities may support colorectal cancer risk assessment through monitoring of bacterial abundance patterns associated with oncological conditions. Clinical research has demonstrated correlations between elevated Streptococcus parasanguinis levels and increased colorectal cancer risk, particularly when present alongside flavonoid-degrading gut bacteria. The spectral sensing system may identify these bacterial abundance patterns through oral cavity analysis, providing non-invasive screening capabilities for colorectal cancer risk assessment.

[0197] The periodontal disease monitoring applications may utilize Streptococcus parasanguinis abundance measurements to assess the progression of aggressive periodontal conditions. Elevated levels of this bacterial species may correlate with bone loss and tissue destruction associated with advanced periodontal disease. The continuous monitoring capability may track bacterial abundance changes that precede clinical manifestations of periodontal tissue damage, enabling preventive interventions that may reduce the risk of tooth loss and associated complications.

[0198] The correlation between severe periodontal disease and all-cause mortality risk may extend the clinical relevance of Streptococcus parasanguinis monitoring beyond oral health applications. The systemic health implications of periodontal disease may be assessed through continuous bacterial abundance monitoring, providing healthcare providers with additional risk stratification information for cardiovascular and metabolic health management.

[0199] The Porphyromonas gingivalis prediction capabilities may enable monitoring of neurodegenerative disease risk, particularly Alzheimer's disease pathology. Clinical studies have documented correlations between elevated oral abundance of Porphyromonas gingivalis and amyloidbeta deposition in brain tissue. The spectral sensing system may detect abundance levels of this bacterial species through daily oral cavity monitoring, providing potential early indicators of neurodegenerative disease risk.

[0200] The relationship between Porphyromonas gingivalis abundance and cognitive decline markers may enable the system to support neurological health monitoring applications. Changes in bacterial abundance levels may correlate with disease progression markers, enabling healthcare providers to implement therapeutic interventions during early disease stages when treatments may be more effective. The continuous monitoring approach may provide longitudinal data that tracks bacterial abundance changes in relation to cognitive function assessments. Attorney Reference No. 444365.000020

[0201] Device Form Factors and Applications

[0202] The device may be available in versatile formats including one or more detachable or standalone sensors, adaptable for integration into rings, bracelets, underwear, VR/AR glasses, bionic lenses, tooth implants, toothbrushes, and other oral hygiene devices. The device may be capable of 24/7 monitoring and continuous data collection, and may be charged using the body's electricity or by using standard chargers.

[0203] The device may be used to scan the oral cavity in a 360-degree manner, with or without the use of a magnifier or fisheye lens as needed, and can be utilized with or without direct contact with the skin, mouth, tongue, or other body parts.

[0204] Medical and Diagnostic Applications

[0205] The device may be used as a standalone medical device for disease detection, prediction, monitoring or prevention, or in conjunction with accompanying software. The device and the accompanying software may also be used for monitoring, prevention and prediction of disease remission or acute inflammatory states, standalone or used together.

[0206] The device may be utilized for assessing and predicting the pace of aging, DNA methylation, blood metabolome, biological processes related to aging, hallmarks of aging, organ health and microbiomes, skin, oral and gut microbiome, mycobiome and virome, blood glucose levels, immune system deviations, inflammation, gut microbiome function, breath, tongue, saliva, oral cavity health, and mental, emotional, and cognitive functions.

[0207] The device may be configured for use in personalized nutrition, medical therapies, wellness monitoring, early disease detection, and optimization of physical and mental performance. The device may be capable of being used in non-invasive diagnostic applications, including, but not limited to, mapping, predicting and modulating the gut-brain axis, brain default mode network, neural circuits, blood-brain barrier and brain secretome, cognitive function, as well as sociological, psychological, emotional, and behavioral patterns.

[0208] Advanced Integration Capabilities

[0209] The device may interface with or integrate into engineered or grown cells and tissues permanently or temporarily entering biological systems, for gathering and sharing information with other systems, as well as with brain computer interface solutions.

[0210] The device, on its own or through associated software and predictive models, may be integrated into self-driving cars, mirrors, toilets, hotels, smart home systems, fitness equipment, public transportation, personal assistants, augmented reality (AR) and virtual reality (VR) systems, retail environments, healthcare facilities, and entertainment venues, among other applications.

[0211] Predictive Modeling Capabilities Attorney Reference No. 444365.000020

[0212] The device may employ predictive models trained on data obtained from the device, enabling assessments and predictions without the physical presence of the device. These assessments and predictions may be made directly through the device itself, or alternatively, through predictive models trained on the data obtained from the device, potentially removing the need for the physical device in future applications.

[0213] Smartphone cameras in conjunction with DL may be used to replicate the diagnostic capabilities of the spectral device, limiting the dependency on the physical device. The system may achieve over 95% accuracy in color reconstruction tasks, with a correlation coefficient of 0.99, even under varying lighting conditions. This high level of accuracy may be beneficial in medical diagnostics, where precise color and texture analysis can provide insights into patient health.

[0214] Performance Metrics and Validation

[0215] Metrics such as accuracy, precision, recall, and Fl -score may be employed to evaluate the model's performance and its ability to generalize across unseen data. Insights gained from testing phases may feed back into further model tuning, ensuring that the final product meets requirements for accuracy and reliability.

[0216] The system may demonstrate improved correlation values through various processing methods. Initial measurements may show correlation values that can be improved through color correction procedures. Using SPD sensor readings alone, the system may achieve higher correlation values compared to camera-based measurements. Machine learning approaches may further improve these correlation values, showing that the correlation between reference and measured color approaches nearperfect alignment.

[0217] The device represents a significant advancement in multispectral and hyperspectral imaging technology, as well as camera-based PPG sensors, offering a high-tech yet accessible solution for a wide range of professional applications, including but not limited to integrative medicine, dentistry, dermatology, neurology, oncology, ophthalmology, cardiology, psychology and other non-invasive diagnostic applications in medical settings.

[0218] Alternative Examples and Applications

[0219] The integrative medicine applications may utilize the multimodal bacterial prediction system to assess overall health status through comprehensive microbiome profiling. The system may provide healthcare practitioners with continuous bacterial abundance data that complements traditional diagnostic approaches. The non-invasive monitoring capability may enable frequent assessment of microbial balance without the cost and time constraints associated with laboratory-based microbiome analysis.

[0220] The dental applications may encompass continuous monitoring of oral cavity health through bacterial abundance tracking that correlates with various dental and periodontal conditions. The system Attorney Reference No. 444365.000020 may detect bacterial imbalances that precede clinical manifestations of dental disease, enabling preventive interventions that may reduce the need for invasive dental procedures. The real-time monitoring capability may provide dental practitioners with objective measures of treatment effectiveness and patient compliance with oral hygiene protocols.

[0221] The dermatological applications may utilize bacterial abundance monitoring to assess skin health conditions that correlate with oral microbiome composition. The gut-skin axis connections may be evaluated through continuous bacterial monitoring, providing dermatologists with additional diagnostic information for conditions such as acne, eczema, and other inflammatory skin disorders. The system may track bacterial abundance changes in response to dermatological treatments, enabling optimization of therapeutic protocols.

[0222] The neurological applications may extend beyond Alzheimer's disease monitoring to encompass broader neurological health assessment through microbiome-brain axis evaluation. The system may provide neurologists with continuous bacterial abundance data that correlates with various neurological conditions, including mood disorders, anxiety, and cognitive function variations. The non-invasive monitoring approach may complement traditional neurological assessments with objective microbial biomarker data.

[0223] The oncological applications may encompass cancer risk assessment and treatment monitoring through bacterial abundance patterns associated with various cancer types. The system may detect bacterial signatures that correlate with cancer development risk, enabling early screening and preventive interventions. The continuous monitoring capability may track bacterial abundance changes during cancer treatment protocols, providing oncologists with additional biomarker information for treatment response assessment.

[0224] The ophthalmological applications may utilize bacterial abundance monitoring to assess eye health conditions that correlate with systemic microbial balance. The system may detect bacterial patterns associated with inflammatory eye conditions, dry eye syndrome, and other ocular disorders that may be influenced by microbiome composition. The continuous monitoring approach may provide ophthalmologists with objective measures of treatment effectiveness for microbiome-related eye conditions.

[0225] The cardiological applications may encompass cardiovascular health monitoring through bacterial abundance patterns associated with heart disease risk factors. The system may detect bacterial signatures that correlate with cardiovascular inflammation, atherosclerosis development, and other cardiac conditions. The continuous monitoring capability may provide cardiologists with additional risk stratification information that complements traditional cardiovascular assessments.

[0226] The psychological applications may utilize bacterial abundance monitoring to assess mental health conditions through gut-brain axis evaluation. The system may detect bacterial patterns associated Attorney Reference No. 444365.000020 with depression, anxiety, and other mood disorders that correlate with microbiome composition. The continuous monitoring approach may provide mental health practitioners with objective biomarker data that complements traditional psychological assessments.

[0227] The therapeutic response prediction capabilities may enable healthcare providers to assess treatment effectiveness through continuous bacterial abundance monitoring. The system may track changes in bacterial composition in response to various therapeutic interventions, including antibiotic treatments, probiotic supplementation, dietary modifications, and other microbiome -targeted therapies. The real-time monitoring capability may enable rapid adjustment of treatment protocols based on bacterial abundance response patterns.

[0228] The early disease detection capabilities may utilize bacterial abundance trend analysis to identify pathological conditions before clinical symptoms manifest. The system may detect subtle changes in bacterial composition that precede disease development, enabling preventive interventions that may reduce disease severity or prevent disease progression. The continuous monitoring approach may provide healthcare providers with early warning indicators that complement traditional diagnostic methods.

[0229] The non-invasive diagnostic applications may encompass mapping, predicting, and modulating the gut-brain axis through continuous bacterial abundance monitoring. The system may assess bacterial patterns that influence neural communication pathways between the gastrointestinal tract and central nervous system. The gut-brain axis evaluation may provide insights into conditions such as irritable bowel syndrome, inflammatory bowel disease, and neuropsychiatric disorders that involve bidirectional communication between gut microbiota and brain function.

[0230] The brain default mode network assessment capabilities may utilize bacterial abundance data to evaluate neural network connectivity patterns associated with microbiome composition. The system may detect bacterial signatures that correlate with default mode network activity, providing insights into cognitive function, attention regulation, and neurological health status. The continuous monitoring approach may track bacterial abundance changes in relation to cognitive performance assessments and neurological function evaluations.

[0231] The neural circuit prediction capabilities may encompass assessment of neurotransmitter production and neural signaling pathways influenced by bacterial metabolite production. The system may detect bacterial species that produce neurotransmitter precursors or metabolites that influence neural circuit function. The bacterial abundance monitoring may provide insights into conditions such as depression, anxiety, and cognitive disorders that may be influenced by microbiome-derived neural signaling molecules.

[0232] The blood-brain barrier assessment capabilities may utilize bacterial abundance monitoring to evaluate barrier function integrity through microbiome-derived inflammatory markers. The system may Attorney Reference No. 444365.000020 detect bacterial patterns associated with blood-brain barrier permeability changes that may contribute to neurological conditions. The continuous monitoring approach may provide neurologists with biomarker information related to barrier function status and neuroinflammatory processes.

[0233] The brain secretome evaluation capabilities may encompass assessment of brain-derived signaling molecules that influence microbiome composition through bidirectional gut-brain communication. The system may detect bacterial abundance patterns that correlate with brain secretome activity, providing insights into neuroendocrine function and hormonal regulation of microbial communities. The monitoring capability may support endocrinological applications related to stress response, circadian rhythm regulation, and metabolic health assessment.

[0234] The cognitive function assessment applications may utilize bacterial abundance monitoring to evaluate cognitive performance markers associated with microbiome composition. The system may detect bacterial patterns that correlate with memory function, attention capacity, executive function, and other cognitive domains. The continuous monitoring approach may provide objective biomarker data that complements traditional cognitive assessments and neuropsychological evaluations.

[0235] The sociological pattern assessment capabilities may encompass evaluation of social behavior markers that correlate with microbiome composition through gut-brain axis interactions. The system may detect bacterial abundance patterns associated with social anxiety, interpersonal communication patterns, and social cognition functions. The monitoring capability may provide insights into conditions such as autism spectrum disorders and social anxiety disorders that may involve microbiome-brain interactions.

[0236] The psychological pattern evaluation applications may utilize bacterial abundance monitoring to assess emotional regulation, mood stability, and psychological well-being markers associated with microbiome composition. The system may detect bacterial signatures that correlate with psychological resilience, stress response patterns, and emotional processing capabilities. The continuous monitoring approach may provide mental health practitioners with objective biomarker data for psychological health assessment.

[0237] The emotional pattern assessment capabilities may encompass evaluation of emotional regulation mechanisms influenced by microbiome-derived neurotransmitter production and inflammatory signaling. The system may detect bacterial abundance patterns associated with emotional stability, mood regulation, and affective processing functions. The monitoring capability may support psychiatric applications related to mood disorders, emotional dysregulation, and affective spectrum conditions.

[0238] The behavioral pattern prediction applications may utilize bacterial abundance monitoring to assess behavioral tendencies and habit formation patterns influenced by microbiome composition. The system may detect bacterial signatures that correlate with impulse control, decision-making processes, Attorney Reference No. 444365.000020 and behavioral regulation mechanisms. The continuous monitoring approach may provide insights into conditions such as attention deficit hyperactivity disorder, obsessive-compulsive disorder, and other behavioral regulation disorders that may involve microbiome-brain interactions.

[0239] The system integration and deployment configurations may encompass multiple operational modes that enable the spectral sensing device to function across diverse environments and application scenarios. The deployment architecture may support standalone operation, integrated smartphone functionality, continuous monitoring capabilities, and embedded integration within various host systems and devices.

[0240] The standalone deployment configuration may enable the spectral sensing device to operate independently without requiring connection to external processing systems or mobile devices. The standalone mode may incorporate onboard processing capabilities that perform spectral analysis and bacterial abundance predictions using embedded computational resources. Local data storage may maintain measurement histories and prediction results within the device memory systems, enabling operation in environments where wireless connectivity may be limited or unavailable.

[0241] The smartphone integration deployment may leverage existing mobile device capabilities to extend the functionality of the spectral sensing device through coordinated operation between the spectroradiometer hardware and smartphone camera systems. The integration architecture may utilize wireless communication protocols to synchronize data collection between the spectral sensing device and smartphone applications. The smartphone processing capabilities may supplement the spectral analysis through machine learning model execution that combines spectroradiometer measurements with smartphone camera imagery.

[0242] The continuous monitoring deployment configuration may enable 24/7 data collection through automated measurement scheduling and power management systems. The continuous operation mode may implement periodic measurement cycles that capture spectral data at predetermined intervals throughout extended monitoring periods. The automated data collection may proceed without user intervention, maintaining consistent measurement protocols across days, weeks, or months of continuous operation.

[0243] The power management system for continuous monitoring may incorporate multiple charging methodologies to sustain extended operation periods. Standard charging capabilities may utilize conventional power sources including USB charging ports, wireless charging pads, and dedicated power adapters that connect to electrical grid systems. The charging infrastructure may support rapid charging protocols that minimize downtime during power replenishment cycles.

[0244] The body electricity charging capability may harvest electrical energy from biological sources to supplement or replace conventional charging methods. The bioelectric energy harvesting may utilize thermoelectric generators that convert body heat into electrical power for device operation. Piezoelectric Attorney Reference No. 444365.000020 energy harvesting may capture mechanical energy from body movements, breathing patterns, or cardiovascular pulsations to generate electrical power. The bioelectric charging system may enable extended operation periods without requiring external power sources, supporting truly continuous monitoring applications.

[0245] The energy storage system may incorporate rechargeable battery technologies that maintain power reserves for extended operation between charging cycles. The battery management system may optimize power consumption through intelligent scheduling of measurement activities and processing operations. Low-power operational modes may reduce energy consumption during periods of reduced measurement activity while maintaining readiness for immediate data collection when required.

[0246] The wearable device integration may enable incorporation of the spectral sensing device into various personal accessories and clothing items. Ring-mounted configurations may position the spectroradiometer sensors in proximity to finger-based measurement sites, enabling continuous monitoring of peripheral circulation and tissue characteristics. The ring form factor may provide discrete monitoring capabilities that integrate seamlessly with daily activities without requiring conscious user interaction.

[0247] Bracelet and wristband integration configurations may position the spectral sensing device for continuous monitoring of wrist-based measurement sites. The wrist-mounted deployment may enable simultaneous monitoring of multiple physiological parameters including pulse characteristics, skin temperature, and spectral signatures associated with cardiovascular function. The bracelet configuration may incorporate adjustable sizing mechanisms that maintain consistent sensor positioning across different users and wearing conditions.

[0248] The underwear integration deployment may enable continuous monitoring of reproductive organ health and associated physiological parameters through spectral analysis of intimate anatomical regions. The textile-integrated sensors may provide discrete monitoring capabilities that operate continuously without requiring conscious user interaction or lifestyle modifications. The underwear deployment may incorporate washable sensor technologies that maintain functionality through normal clothing care procedures.

[0249] The virtual reality and augmented reality integration may position spectral sensing capabilities within immersive display systems for enhanced diagnostic and monitoring applications. VR headset integration may enable simultaneous spectral analysis and visual display of measurement results within virtual environments. The AR integration may overlay spectral analysis results onto real-world visual scenes, providing contextual information that enhances user understanding of measurement data.

[0250] The bionic lens integration may incorporate spectral sensing capabilities directly within ocular devices that provide vision correction or enhancement. The lens-mounted sensors may enable continuous monitoring of eye health parameters and systemic physiological indicators through spectral Attorney Reference No. 444365.000020 analysis of ocular tissues. The bionic lens deployment may provide discrete monitoring capabilities that operate continuously during normal vision activities.

[0251] The dental integration configurations may incorporate spectral sensing capabilities within oral healthcare devices and dental appliances. Tooth implant integration may position spectroradiometer sensors within artificial tooth structures, enabling continuous monitoring of oral cavity health and bacterial abundance levels. The implant-mounted sensors may provide long-term monitoring capabilities that operate continuously without requiring user intervention or device maintenance.

[0252] Toothbrush integration may incorporate spectral sensing capabilities within oral hygiene devices, enabling routine monitoring during daily dental care activities. The toothbrush-mounted sensors may capture spectral measurements during brushing procedures, providing regular assessment of oral health parameters without requiring additional time or effort from users. The integration may combine oral hygiene functions with diagnostic capabilities in unified device platforms.

[0253] The oral hygiene device integration may extend beyond toothbrushes to encompass various dental care implements including dental floss dispensers, mouthwash delivery systems, and tongue cleaning devices. The integrated sensors may provide comprehensive oral health monitoring that covers multiple anatomical regions and hygiene procedures within coordinated measurement protocols.

[0254] The 360-degree oral cavity scanning capability may enable comprehensive spectral analysis of oral tissues through coordinated sensor positioning and measurement sequencing. The scanning system may incorporate multiple spectroradiometer units positioned at different angles to capture spectral data from various oral cavity surfaces simultaneously. The multi-angle measurement approach may provide complete coverage of tongue surfaces, gum tissues, tooth surfaces, and other oral structures within single scanning procedures.

[0255] The magnifier lens integration may enhance the spatial resolution of oral cavity scanning through optical magnification systems that focus spectral measurements on specific tissue regions. The magnification capability may enable detailed analysis of small tissue areas that exhibit localized pathological changes or bacterial colonization patterns. The magnified scanning may provide enhanced sensitivity for detecting early-stage disease processes or subtle tissue changes that may not be apparent through standard resolution measurements.

[0256] The fisheye lens integration may provide wide-angle spectral imaging capabilities that capture comprehensive oral cavity views within single measurement procedures. The fisheye optical system may enable simultaneous spectral analysis of multiple oral cavity regions, reducing the time required for complete oral health assessments. The wide-angle capability may provide efficient scanning procedures that minimize user discomfort while maintaining comprehensive diagnostic coverage.

[0257] The contact and non-contact measurement capabilities may enable flexible deployment across different anatomical sites and measurement scenarios. Contact measurement modes may position Attorney Reference No. 444365.000020 spectroradiometer sensors directly against tissue surfaces, providing enhanced signal strength and reduced interference from ambient lighting conditions. The direct contact approach may enable measurements of subsurface tissue characteristics through enhanced optical coupling between sensors and biological tissues.

[0258] The non-contact measurement capability may enable spectral analysis without requiring physical contact between sensors and biological tissues. The non-contact mode may utilize focused optical systems that capture spectral signatures from tissue surfaces at distances ranging from millimeters to centimeters. The non-contact approach may provide enhanced user comfort and reduced contamination risks while maintaining measurement accuracy and reliability.

[0259] The brain-computer interface integration may enable direct communication between the spectral sensing device and neural interface systems. The BCI connectivity may facilitate real-time transmission of spectral measurement data to neural processing systems that correlate bacterial abundance levels with cognitive function parameters. The neural interface integration may enable closed-loop monitoring systems that adjust therapeutic interventions based on combined spectral and neural activity measurements.

[0260] The engineered biological system integration may enable incorporation of spectral sensing capabilities within synthetic biological constructs and tissue engineering applications. The biointegrated sensors may be incorporated within engineered tissues that are designed for temporary or permanent implantation within biological systems. The synthetic biology integration may enable continuous monitoring of tissue engineering progress and biological system function through embedded spectral analysis capabilities.

[0261] The grown tissue integration may incorporate spectral sensing capabilities within naturally grown biological tissues through biocompatible sensor implantation procedures. The tissue-integrated sensors may provide continuous monitoring of tissue health and bacterial colonization patterns from within biological systems. The grown tissue deployment may enable long-term monitoring applications that operate continuously throughout tissue development and maturation processes.

[0262] The smart home system integration may incorporate spectral sensing capabilities within residential environments through embedded sensors in bathroom fixtures, kitchen appliances, and personal care devices. Mirror integration may enable routine health monitoring during daily grooming activities, providing spectral analysis results through integrated display systems. The mirror-mounted sensors may capture facial spectral signatures that correlate with systemic health parameters and bacterial abundance levels.

[0263] Toilet integration may enable routine monitoring during bathroom visits, providing discrete health assessments without requiring conscious user interaction. The toilet-mounted sensors may analyze various biological samples and spectral signatures associated with digestive health and Attorney Reference No. 444365.000020 microbiome composition. The bathroom fixture integration may provide regular health monitoring that operates seamlessly within existing daily routines.

[0264] The hotel integration deployment may provide temporary health monitoring capabilities for travelers and temporary residents. Hotel room integration may incorporate spectral sensing capabilities within bathroom fixtures, mirrors, and personal care amenities that provide health assessments during hotel stays. The temporary deployment may enable continuity of health monitoring during travel periods without requiring users to transport personal monitoring devices.

[0265] The fitness equipment integration may incorporate spectral sensing capabilities within exercise machines and workout accessories. Treadmill, bicycle, and weight training equipment integration may enable continuous health monitoring during exercise activities. The fitness integration may provide realtime feedback on physiological parameters and bacterial abundance levels that correlate with exercise performance and recovery processes.

[0266] The personal assistant integration may incorporate spectral sensing capabilities within voice- activated devices and smart speakers that provide health monitoring functions alongside traditional assistant services. The assistant integration may enable voice-controlled health assessments and automated scheduling of monitoring activities. The conversational interface may provide user-friendly access to spectral analysis results and health recommendations based on bacterial abundance measurements.

[0267] The healthcare facility integration may incorporate spectral sensing capabilities within clinical environments including hospitals, clinics, and diagnostic laboratories. The clinical deployment may provide enhanced diagnostic capabilities that supplement traditional medical assessments with continuous bacterial abundance monitoring. The healthcare integration may enable population health monitoring and infection control applications through comprehensive spectral surveillance systems.

[0268] The spectral sensing system may be implemented through various alternative configurations that provide different approaches to multispectral data capture, processing, and analysis. Alternative sensor architectures may incorporate different numbers of spectral channels, wavelength ranges, and detection methodologies to accommodate diverse application requirements and performance specifications.

[0269] Single-channel spectroradiometer configurations may utilize broadband optical sensors with tunable filter systems that sequentially capture spectral measurements across multiple wavelength ranges. The tunable filter approach may employ liquid crystal tunable filters, acousto-optic tunable filters, or mechanically rotating filter wheels that provide wavelength selectivity through temporal scanning procedures. The sequential measurement approach may reduce hardware complexity while maintaining spectral resolution capabilities across extended wavelength ranges.

[0270] Multi-pixel detector arrays may replace single-pixel detection systems to provide simultaneous spatial and spectral measurements. The array-based sensors may incorporate charge -coupled device Attorney Reference No. 444365.000020

(CCD) or complementary metal-oxide-semiconductor (CMOS) detector technologies with integrated spectral filtering elements. Each pixel within the detector array may be configured with different spectral response characteristics, enabling simultaneous capture of multiple wavelength channels across spatial regions of interest.

[0271] Hyperspectral line-scan configurations may provide enhanced spectral resolution through dispersive optical elements including diffraction gratings or prisms that separate incident light into constituent wavelength components. The line-scan approach may capture complete spectral signatures across hundreds of contiguous wavelength bands, providing detailed spectral characterization that exceeds the capabilities of discrete filter-based systems. The hyperspectral data may enable more sophisticated spectral analysis techniques including spectral unmixing and material identification algorithms.

[0272] Interferometric spectral sensing approaches may utilize Fourier transform infrared (FTIR) or Fabry-Perot interferometer configurations to achieve enhanced spectral resolution and sensitivity. The interferometric methods may provide wavelength accuracy and resolution that exceeds filter-based approaches while maintaining compact form factors suitable for portable applications. The interferometric sensing may enable detection of narrow spectral features that correspond to specific molecular absorption or emission signatures.

[0273] Alternative machine learning architectures may encompass various neural network designs beyond the ResNet and convolutional neural network approaches described previously. Transformerbased architectures, such as Vision Transformers (ViT), may process spectral images through attention mechanisms that identify relevant spectral features and correlations across different wavelength channels. For applications involving longitudinal data, Recurrent Neural Network architectures, including Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) configurations, may be employed to process temporal sequences of spectral measurements to capture dynamic changes in biological markers over time. Furthermore, Graph Neural Network architectures may be used to model complex relationships between different spectral channels, spatial regions, and temporal measurements through graph-based representations, allowing for the integration of prior knowledge about biological processes into the machine learning framework. The attention-based processing may enable the model to focus on specific spectral regions that provide the most informative content for bacterial abundance prediction tasks.

[0274] Vision transformer (ViT) architectures may process spectral images through patch-based tokenization procedures that divide input images into discrete spatial regions for sequential processing. Each image patch may be treated as a token within a sequence that is processed through multi-head attention mechanisms. The transformer approach may capture long-range spatial dependencies within Attorney Reference No. 444365.000020 spectral images that may not be effectively addressed through conventional convolutional processing methods.

[0275] Recurrent neural network architectures including long short-term memory (LSTM) and gated recurrent unit (GRU) configurations may process temporal sequences of spectral measurements to capture dynamic changes in bacterial abundance levels over time. The recurrent processing may enable the model to learn temporal patterns and trends that provide enhanced prediction accuracy compared to single-timepoint analysis approaches. The temporal modeling may be particularly valuable for applications that monitor bacterial abundance changes in response to therapeutic interventions or lifestyle modifications.

[0276] Graph neural network architectures may model relationships between different spectral channels, spatial regions, and temporal measurements through graph-based representations. The graphbased approach may capture complex interdependencies between different data modalities that may not be effectively represented through conventional neural network architectures. The graph processing may enable the integration of prior knowledge about spectral relationships and biological processes into the machine learning framework.

[0277] Ensemble learning approaches may combine predictions from multiple different model architectures to achieve enhanced accuracy and robustness compared to single-model approaches. The ensemble methods may include bagging approaches that train multiple models on different subsets of the training data, boosting methods that sequentially train models to correct errors from previous models, and stacking approaches that combine predictions from different model types through meta-leaming procedures.

[0278] Gaussian process regression methods may provide probabilistic predictions that include uncertainty quantification for bacterial abundance estimates. The Gaussian process approach may model the underlying function that maps spectral measurements to bacterial abundance values through probabilistic distributions that capture both prediction accuracy and confidence intervals. The uncertainty quantification may provide valuable information for clinical decision-making applications where prediction confidence is important for treatment decisions.

[0279] Support vector machine (SVM) configurations may provide alternative regression approaches for bacterial abundance prediction through kernel-based methods that map input features into higherdimensional spaces. The SVM approach may utilize radial basis function kernels, polynomial kernels, or custom kernel functions that are designed to capture specific relationships between spectral features and bacterial abundance values. The kernel-based processing may enable effective handling of nonlinear relationships without requiring deep neural network architectures.

[0280] Alternative spectral reconstruction methods may encompass various computational approaches for predicting extended spectral information from limited input measurements. Principal component Attorney Reference No. 444365.000020 analysis (PCA) methods may identify the most significant spectral variations within training datasets and use these principal components to reconstruct complete spectral signatures from reduced- dimensionality measurements. The PCA approach may provide dimensionality reduction that preserves the most informative spectral content while reducing computational requirements.

[0281] Independent component analysis (ICA) methods may separate spectral measurements into statistically independent components that correspond to different physical or biological processes. The ICA approach may enable the identification of spectral signatures associated with specific bacterial species or physiological conditions that are mixed within the overall spectral measurements. The component separation may provide enhanced specificity for bacterial abundance prediction tasks.

[0282] Non-negative matrix factorization (NMF) approaches may decompose spectral measurements into non-negative basis functions and corresponding coefficients that represent the contributions of different spectral components. The NMF method may be particularly suitable for spectral data where negative values are not physically meaningful, such as optical absorption or emission measurements. The factorization approach may identify spectral basis functions that correspond to specific bacterial species or tissue characteristics.

[0283] Sparse coding methods may represent spectral measurements as sparse linear combinations of basis functions selected from overcomplete dictionaries. The sparse representation may identify the most relevant spectral features for bacterial abundance prediction while reducing the influence of noise and irrelevant spectral content. The sparse coding approach may provide enhanced robustness to measurement variations and environmental interference.

[0284] Dictionary learning approaches may automatically discover optimal basis functions for spectral representation through iterative optimization procedures that minimize reconstruction errors while maintaining sparsity constraints. The learned dictionaries may capture spectral patterns that are specifically relevant to bacterial abundance prediction tasks, providing enhanced performance compared to generic basis function sets.

[0285] Alternative multimodal data fusion approaches may encompass various methods for combining information from different data sources including spectral measurements, conventional photography, and user-reported data. Early fusion methods may combine raw data from different modalities at the input level before processing through unified machine learning models. The early fusion approach may enable the model to learn cross-modal relationships and interactions throughout the entire processing pipeline.

[0286] Late fusion methods may process each data modality through separate specialized models and combine the resulting predictions through weighted averaging, voting procedures, or meta-leaming approaches. The late fusion strategy may enable the optimization of processing methods for each individual data modality while maintaining flexibility in the combination procedures. The separate Attorney Reference No. 444365.000020 processing may provide enhanced robustness to variations in data quality or availability across different modalities.

[0287] Intermediate fusion approaches may combine features extracted from different data modalities at intermediate processing stages within the machine learning pipeline. The intermediate fusion may occur after initial feature extraction but before final prediction layers, enabling the model to learn cross- modal feature interactions while maintaining some degree of modality-specific processing. The intermediate approach may provide a balance between early and late fusion strategies.

[0288] Attention-based fusion methods may utilize attention mechanisms to dynamically weight the contributions of different data modalities based on their relevance to specific prediction tasks. The attention-based approach may enable the model to focus on the most informative data sources for each individual prediction while maintaining the ability to utilize information from all available modalities. The dynamic weighting may provide enhanced adaptability to variations in data quality and relevance across different measurement scenarios.

[0289] Cross-modal learning approaches may enable the model to learn representations that are shared across different data modalities, facilitating knowledge transfer and improved generalization performance. The cross-modal representations may capture underlying biological or physical processes that are reflected in multiple data sources, providing enhanced robustness to missing or corrupted data from individual modalities.

[0290] Alternative device form factors may encompass various physical configurations that accommodate different application requirements and user preferences. Patch-based sensors may provide adhesive-mounted devices that attach directly to skin surfaces for continuous monitoring applications. The patch configuration may incorporate flexible substrates and conformable designs that maintain sensor contact during body movement and daily activities.

[0291] Implantable sensor configurations may provide subcutaneous or intracavitary monitoring capabilities through biocompatible devices that are surgically placed within biological tissues. The implantable approach may enable continuous monitoring without external device requirements while providing enhanced signal quality through direct tissue contact. The implanted sensors may incorporate wireless power transfer and data communication capabilities that eliminate the need for physical connections to external systems.

[0292] Ingestible sensor capsules may provide temporary internal monitoring capabilities through swallowable devices that transit through the gastrointestinal system while collecting spectral measurements. The capsule approach may enable direct assessment of internal tissue characteristics and bacterial populations that are not accessible through external sensing methods. The ingestible sensors may incorporate biodegradable materials that safely dissolve after completing measurement procedures. Attorney Reference No. 444365.000020

[0293] Textile-integrated sensor configurations may incorporate spectral sensing capabilities within clothing fibers and fabric structures. The textile integration may provide discrete monitoring capabilities that operate continuously during normal clothing wear without requiring conscious user interaction. The fabric-based sensors may utilize conductive fibers, optical fibers, or embedded electronic components that maintain functionality through normal clothing care procedures including washing and drying.

[0294] Jewelry-integrated configurations may extend beyond ring and bracelet implementations to include necklace-mounted sensors, earring-based devices, and pendant configurations that provide various anatomical positioning options. The jewelry integration may provide aesthetically pleasing monitoring devices that blend seamlessly with personal accessories while maintaining spectral sensing capabilities.

[0295] Contact lens integration may incorporate spectral sensing capabilities within ocular devices that provide continuous eye health monitoring and systemic physiological assessment through tear film analysis. The contact lens sensors may utilize transparent optical elements and miniaturized electronics that maintain visual clarity while enabling spectral measurements of ocular tissues and tear composition. [0296] Alternative integration methods may encompass various approaches for incorporating spectral sensing capabilities within existing devices and systems. Modular sensor attachments may provide addon capabilities that connect to smartphones, tablets, or other electronic devices through standard interface connections including USB, Lightning, or wireless protocols. The modular approach may enable users to add spectral sensing capabilities to existing devices without requiring complete system replacement.

[0297] Software-defined sensor configurations may utilize programmable hardware platforms that can be reconfigured for different spectral sensing applications through software updates. The software- defined approach may enable the same hardware platform to support multiple different sensing modes, wavelength ranges, and analysis algorithms through downloadable configuration files. The programmable sensors may provide enhanced flexibility and upgradability compared to fixed-function hardware implementations.

[0298] Cloud-based processing architectures may offload computational requirements from local devices to remote server systems that provide enhanced processing capabilities and storage capacity. The cloud processing approach may enable the use of more sophisticated machine learning models and larger training datasets while reducing the computational requirements for local devices. The cloud architecture may provide automatic software updates and model improvements without requiring local device modifications.

[0299] Edge computing configurations may provide local processing capabilities that reduce latency and bandwidth requirements while maintaining privacy and security for sensitive health data. The edge processing approach may incorporate specialized processors including graphics processing units Attorney Reference No. 444365.000020

(GPUs), tensor processing units (TPUs), or field-programmable gate arrays (FPGAs) that provide enhanced computational performance for machine learning applications.

[0300] Alternative deployment scenarios may encompass various operational environments and use cases that extend beyond individual health monitoring applications. Population health surveillance systems may utilize networks of spectral sensing devices to monitor bacterial abundance patterns across large populations for epidemiological research and public health applications. The population-level monitoring may enable early detection of disease outbreaks and assessment of intervention effectiveness across diverse communities.

[0301] Environmental monitoring applications may utilize spectral sensing capabilities to assess bacterial contamination in water supplies, food products, and environmental samples. The environmental deployment may provide rapid bacterial detection capabilities that complement traditional laboratory-based testing methods while providing real-time monitoring capabilities for contamination events.

[0302] Agricultural applications may utilize spectral sensing for monitoring plant health, soil microbiome composition, and crop disease detection through bacterial abundance assessment. The agricultural deployment may provide farmers with real-time information about crop health status and disease risk factors that enable proactive intervention strategies.

[0303] Veterinary applications may extend bacterial abundance monitoring to animal health assessment through spectral analysis of animal tissues and biological samples. The veterinary deployment may provide non-invasive health monitoring capabilities for livestock, companion animals, and wildlife populations through adapted sensor configurations and analysis algorithms.

[0304] Industrial process monitoring applications may utilize spectral sensing for quality control and contamination detection in manufacturing environments including pharmaceutical production, food processing, and biotechnology applications. The industrial deployment may provide continuous monitoring capabilities that ensure product quality and safety while reducing the need for manual sampling and laboratory testing procedures.

[0305] Research applications may utilize spectral sensing capabilities for basic science investigations including microbiome research, disease mechanism studies, and therapeutic development programs. The research deployment may provide enhanced data collection capabilities that enable large-scale studies and longitudinal investigations that were previously limited by traditional sampling and analysis methods.

[0306] Referring now to FIGs. 12A-12F, different views of a spectral sensing device is shown. FIG. 12A is a wireframe component diagram of the spectral sensing device. FIG. 12B is a first perspective view of the spectral sensing device. Attorney Reference No. 444365.000020

[0307] FIG. 12C is a bottom view of the spectral sensing device. FIG. 12D is a second perspective view of the spectral sensing device. FIG. 12E is a perspective view of internal components of the spectral sensing device. FIG. 12F is a top view of the internal components of the spectral sensing device.

[0308] The spectral sensing device may include an enclosure 1202. The enclosure 1202 may protect and securely hold all internal components in place. This enclosure 1202 may be 3D printed using PETG thermoplastic. The enclosure may be assembled with a single M2 hex head screw that fastens into an M2 brass insert, which may be heat-pressed into the enclosure.

[0309] The spectral sensing device may include a charging port 1204. The charging port 1204 may be a connector for one more of charging a battery 1210, auxiliary data transfer, and firmware updates.

[0310] The spectral sensing device may include a transparent cover 1206. The transparent cover 1206 may be made of glass. The transparent cover 1206 may protect sensitive components while providing a clear line of sight for a single pixel detector 1218.

[0311] The spectral sensing device may include an antenna 1208. The antenna 1208 may be a 2.4GHz antenna and may be configured to extend a wireless communication range at the 2.4 GHz frequency used by the Bluetooth protocol.

[0312] The spectral sensing device may include the battery 1210. The battery 1210 may provide power to the device for portable use, delivering a nominal voltage of 3.7V and a capacity of 500mAh. The battery 1210 may be equipped with a protection circuit that safeguards against overvoltage, overcurrent, short circuits, and over-discharge.

[0313] The spectral sensing device may include an M2 hole 1212. The M2 hole 121 may act as a mechanical fixture point for securing enclosure with M2 hex screw.

[0314] The spectral sensing device may include a microcontroller 1214. The microcontroller 1214 may be a central processing unit configured to oversee device operations and interfaces, read sensor data, and manage connectivity. The microcontroller 1214 may include an ESP32-S3 dual-core Xtensa LX7 processor running at 240 MHz, supporting WiFi 4 and Bluetooth 5.0 LE wireless connectivity, as well as UART, I2C, and SPI interfaces. Additionally, the microcontroller 1214 may offer deep sleep modes with ultra-low power consumption (14 pA), essential for wearable devices.

[0315] The spectral sensing device may include an antenna connector 1216, which may serve as a connection point for attaching the antenna 1208 to internal circuitry of the microcontroller 1214.

[0316] The spectral sensing device may include the single pixel detector module 1218. The single pixel detector module 121 may be an AS7341 module that includes an 11-channel spectral color sensor primarily used for visible light component measurement across multiple bands in the environment. The single pixel detector module 121 may include 8 visible spectrum channels, a near-infrared (NIR) channel, and a no-filter channel, along with a dedicated flicker detection channel. The single pixel detector module 121 may have high sensitivity and accuracy in a small form factor, making it suitable Attorney Reference No. 444365.000020 for miniature spectrometer applications. The single pixel detector module 121 may have an integrated LED light for measurements in low-light environments and may support programmable gain for integrated analog to digital converters making it suitable to use in diverse light conditions.

[0317] The spectral sensing device may include a button 1220. The button 1220 may be used to turn on the spectral sensing device from deep sleep and start a measurement. The spectral sensing device may automatically turn off after measured data is transmitted (e.g., to phone application).

[0318] The following dimensions are exemplary: dl may be approximately 30mm, d2 may be approximately 15mm, d3 may be approximately 13mm, d4 may be approximately 52mm, d5 may be approximately 3.5mm, d6 may be approximately 1.62mm, d7 may be approximately 2mm, d8 may be approximately 30mm, d9 may be approximately, 15.5mm, dlO may be approximately 8.6mm, dl l may be approximately 14.1mm, dl2 may be approximately9. 1mm, dl3 may be approximately 2.9mm, dl4 may be approximately 26.16mm. The radius R1 of the edge may be approximately 1mm.

[0319] Referring now to FIG. 13, The term “computer” shall refer to any electronic device or devices, including those having capabilities to be utilized in connection with an electronic information / transaction system, such as any device capable of receiving, transmitting, processing and/or using data and information. The computer may comprise a server, a processor, a microprocessor, a personal computer, such as a laptop, palm PC, desktop or workstation, a network server, a mainframe, an electronic wired or wireless device, such as for example, a telephone, a cellular telephone, a personal digital assistant, a smartphone, an interactive television, such as for example, a television adapted to be connected to the Internet or an electronic device adapted for use with a television, an electronic pager or any other computing and/or communication device.

[0320] The term “network” shall refer to any type of network or networks, including those capable of being utilized in connection with the systems and methods described herein, such as, for example, any public and/or private networks, including, for instance, the Internet, an intranet, or an extranet, any wired or wireless networks or combinations thereof.

[0321] The term “computer-readable storage medium” should be taken to include a single medium or multiple media that store one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing or encoding a set of instructions for execution by the machine and that causes the machine to perform any one or more of the methodologies of the present disclosure.

[0322] Referring now to FIG. 13, a component diagram of a machine in the example form of computer system 1300 within which a set of instructions for causing the machine to perform any one or more of the methodologies, processes or functions discussed herein may be executed. In some examples, the machine may be connected (e.g., networked) to other machines as described above. The machine may operate in the capacity of a server or a client machine in a client-server network environment, or as a Attorney Reference No. 444365.000020 peer machine in a peer-to-peer (or distributed) network environment. The machine may be any specialpurpose machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine for performing the functions described herein. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. In some examples, one or more of components of the digital platform 100 may be implemented by a specialized machine, particularly programmed to perform certain functions, such as the example machine shown in FIG. 13 (or a combination of two or more of such machines).

[0323] The example computer system 1300 may include processing device 1302, memory 1306, data storage device 1310 and communication interface 1312, which may communicate with each other via data and control bus 1318. In some examples, computer system 1300 may also include display device 1314 and/or user interface 1316.

[0324] Display device 1314 may be any known display technology, including but not limited to display devices using Liquid Crystal Display (LCD) or Light Emitting Diode (LED) technology.

[0325] The processing device 1302 may be one or more processors that use any known processor technology, including but not limited to graphics processors and multi-core processors. The processing device 1302 may include, without being limited to, a microprocessor, a central processing unit, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP) and/or a network processor. The processing device 1302 may be configured to execute processing logic 1304 for performing the operations described herein. The processing device 1302 may include a special -purpose processing device specially programmed with processing logic 1304 to perform the operations described herein.

[0326] The memory 1306 may include, for example, without being limited to, at least one of a readonly memory (ROM), a random access memory (RAM), a flash memory, a dynamic RAM (DRAM) and a static RAM (SRAM), storing computer-readable instructions 1308 executable by processing device 1302. The memory 1306 may include a non-transitory computer readable storage medium storing computer-readable instructions 1308 executable by processing device 1302 for performing the operations described herein. For example, the computer-readable instructions 1308 may include operations performed by components of the digital platform 100. Although one memory 1306 is illustrated in FIG. 13, in some examples, computer system 1300 may include two or more memory devices (e.g., dynamic memory and static memory).

[0327] The user interface 1316 may be any known input device technology, including but not limited to a keyboard (including a virtual keyboard), mouse, track ball, camera, augmented and/or virtual reality devices, connected intemet-of-things (“loT”) devices, and a touch- sensitive pad or display. Attorney Reference No. 444365.000020

[0328] The data and control bus 1318 may be any known internal or external bus technology, including but not limited to industry standard architecture (ISA), extended ISA (EISA), peripheral component interconnect (PCI), PCI Express, universal serial bus (USB), Serial advanced technology attachment (ATA) or FireWire.

[0329] The computer system 1300 may include communication interface 1312, for direct communication with other computers (including wired and/or wireless communication) and/or for communication with a network. In some examples, computer system 1300 may include display device 1314 (e.g., a liquid crystal display (LCD), a touch sensitive display, etc.).

[0330] In some examples, the computer system 1300 may include data storage device 1310 storing instructions (e.g., software) for performing any one or more of the functions described herein. Data storage device 1310 may include a non-transitory computer-readable storage medium, including, without being limited to, solid-state memories, optical media and magnetic media.

[0331] One or more features or steps of the disclosed examples may be implemented using an API. An API may define one or more parameters that are passed between a calling application and other software code (e.g., an operating system, library routine, function) that provides a service, that provides data, or that performs an operation or a computation.

[0332] The API may be implemented as one or more calls in program code that send or receive one or more parameters through a parameter list or other structure based on a call convention defined in an API specification document. A parameter may be a constant, a key, a data structure, an object, an object class, a variable, a data type, a pointer, an array, a list, or another call. API calls and parameters may be implemented in any programming language. The programming language may define the vocabulary and calling convention that a programmer will employ to access functions supporting the API.

[0333] In some implementations, an API call may report to an application the capabilities of a device running the application, such as input capability, output capability, processing capability, power capability, communications capability, etc.

[0334] Referring now to FIG. 14, a flowchart illustrating a method 1400 of predicting microbiome composition from non-invasive multimodal data sources is shown. The method 1400 is representative example and is not intended to be limiting. In the method 1400, spectral image data, photographic image data, and tabular input data (e.g., questionnaire responses) are separately processed by respective encoder modules to extract modality-specific features. The outputs of these encoders are combined in a feature fusion stage and processed by a prediction module configured to estimate one or more microbiome composition parameters, such as bacterial abundance or relative proportions of taxa. Variations in architecture, data modalities, processing steps, or output formats may be used without departing from the scope of the invention. Attorney Reference No. 444365.000020

[0335] The method 1400 begins at step 1402 with acquiring data from three input sources. At step 1404, the method processes spectral image data, followed by step 1406 where standard RGB photo data is processed. The method 1400 continues to step 1408 where user-reported tabular data is processed.

[0336] At step 1410, the method 1400 performs feature concatenation of the processed data. Step 1412 involves applying a bacterial abundance predictor to the concatenated features. The method 1400 then proceeds to step 1414 where predicted bacterial abundances are generated.

[0337] At step 1416, the method 1400 includes a decision point to determine whether to perform longitudinal trend analysis. If the decision is affirmative, the method 1400 proceeds to step 1418 where temporal microbiome composition trends are generated. Following either the trend analysis or a negative decision at step 1416, the method 1400 concludes at step 1420 with outputting microbiome composition prediction results.

[0338] The flowchart shows the sequential processing of multiple data types and the optional longitudinal analysis path, illustrating how different data sources are combined and analyzed to generate microbiome composition predictions.

[0339] Referring now to FIG. 15, a flowchart of a method 1500 for generating a health risk assessment based on predicted microorganism abundance is shown. The method 1500 begins with Step 1502, obtaining predicted microorganism abundance. The method 1500 then proceeds to Step 1504, comparing the abundance to a predetermined threshold. Following the comparison, the method 1500 moves to Step 1506, a decision point where it is determined whether the abundance is above the threshold. If the abundance is above the threshold (Yes branch), the process proceeds to Step 1508, generating a high risk score. If the abundance is not above the threshold (No branch), the process moves to Step 1510, of generating a low risk score. After either risk score generation step, the method 1500 converges to Step 1512, outputting the health risk assessment.

[0340] The flowchart depicts a sequential process with a single decision point that determines the risk categorization. The method 1500 incorporates a binary classification approach based on the comparison of predicted microorganism abundance to a predetermined threshold. This approach allows for a straightforward risk assessment output. The process demonstrates a structured method for translating quantitative microorganism abundance data into a qualitative health risk assessment.

[0341] Referring now to FIG. 16, a system diagram showing a model trained on SPD data that is deployable on RGB-only devices is shown. The system includes a camera 1600 configured to capture RGB images. The camera 1600 provides input to a spectral reconstruction model 1602 that processes the RGB image data. The spectral reconstruction model 1602 includes multiple reconstruction blocks arranged in sequence, including a first spectral reconstruction block 1612, a second spectral reconstruction block 1614, and a third spectral reconstruction block 1616. Attorney Reference No. 444365.000020

[0342] The spectral reconstruction model 1602 is trained using SPD data 1608 and can be deployed on RGB-only devices. The output of the spectral reconstruction model 1602 is a multispectral image stack 1604 containing reconstructed spectral information. The system demonstrates how RGB image data can be transformed into multispectral data through the trained model architecture.

[0343] The diagram shows the sequential processing flow from input RGB image capture through the spectral reconstruction blocks to generate the final multispectral output. The arrangement of components illustrates how the system processes standard RGB images to produce enhanced spectral information using a model trained on reference SPD measurements.

[0344] It should be understood that the arrangement shown in FIG. 16 is provided as a non-limiting example example, and variations in the number, type, order, or configuration of components, as well as in the specific training methodology, may be used without departing from the scope of the invention.

[0345] In an example, the device, systems, and methods described above may be utilized as an attachment and software system for smartphones or dedicated handheld devices, and more particularly to a system for capturing and analyzing multi-spectral images of animal tissues across multiple (e.g., at least 8) spectral bands plus near-infrared (NIR) to compute physiological and biomarker indices using on-device or cloud machine learning.

[0346] Conventional pet diagnostic methods often rely on visual examinations, which can be subjective and may overlook subtle health issues. These examinations typically require experienced veterinarians and may not detect early-stage conditions. Blood work, while informative, is invasive and can be costly, potentially causing stress to the animal and requiring specialized laboratory analysis. Thermal imaging, another diagnostic tool, necessitates expensive equipment and specialized training to interpret results accurately.

[0347] In recent years, there has been growing interest in non-invasive diagnostic technologies for pet health assessment. Multispectral imaging has emerged as a promising approach, offering the potential to capture detailed information about tissue properties without the need for invasive procedures. However, existing multispectral systems often involve complex arrangements of prisms, gratings, and filters, making them bulky, expensive, and impractical for widespread use in veterinary settings.

[0348] The integration of advanced sensing technologies with consumer electronics, particularly smartphones, has opened new possibilities for pet health monitoring. Smartphone cameras, while capable of capturing high-quality images, are limited in their ability to detect spectral information beyond the visible range. This limitation has prompted research into methods for expanding the spectral sensing capabilities of mobile devices.

[0349] Color accuracy and consistency in diagnostic imaging present ongoing challenges, particularly when relying on consumer-grade cameras. Variations in lighting conditions and device-specific color Attorney Reference No. 444365.000020 processing can lead to inconsistencies in image interpretation. Addressing these issues is crucial for developing reliable diagnostic tools that can be used in diverse environments.

[0350] Near-infrared (NIR) imaging has shown promise in various biomedical applications, including the assessment of blood flow and subcutaneous tissue properties. Incorporating NIR capabilities into compact, accessible devices could enhance the depth and quality of information available for pet health assessment.

[0351] As pet ownership continues to rise globally, there is an increasing demand for accessible, accurate, and non-invasive health monitoring tools. Pet owners and veterinarians alike seek solutions that can provide early detection of health issues, facilitate regular monitoring, and improve overall pet care without causing undue stress to the animals.

[0352] The development of compact, multispectral imaging systems that can integrate with widely available consumer devices represents a promising direction for advancing pet health diagnostics. Such systems could potentially overcome many of the limitations associated with traditional diagnostic methods, offering a balance of accuracy, accessibility, and ease of use.

[0353] According to an aspect of the present disclosure, a multispectral sensing device is provided. The device includes a sensor array comprising multiple microscale optical filters, each filter tuned to isolate a specific spectral band. The device is configured to capture images at eight distinct wavelengths. The device further includes a near-infrared (NIR) sensor centered at 910 nm for applications such as blood flow monitoring and subcutaneous imaging up to a depth of 5mm.

[0354] According to other aspects of the present disclosure, the device may include one or more of the following features. The device may be integrated with a smartphone camera. The device may achieve over 95% accuracy in color reconstruction tasks, with a correlation coefficient (r) of 0.99, even under varying lighting conditions. The device may include a color-correction Look-Up Table (LUT) combined with a linear matrix model for fast RGB reconstruction from spectral power distribution (SPD) channels. The device may implement deep-learning spectral reconstruction methods, including Random Forest or Convolutional Neural Network (CNN) models.

[0355] According to another aspect of the present disclosure, a method for analyzing biological tissue using multispectral imaging is provided. The method includes capturing multispectral data of biological tissue using a multispectral sensing device, processing the captured data using machine learning algorithms, and generating output including risk scores, trend graphs, and a veterinarian-shareable report that is FHIR compliant.

[0356] According to other aspects of the present disclosure, the method may include one or more of the following features. The method may include detecting and analyzing biomarkers including tongue pallor as an indicator for anemia risk, gingival color as an indicator for hydration or perfusion, facial thermal pattern in Near- Infrared (NIR) as an indicator for inflammation, and coat lustre index as an indicator for Attorney Reference No. 444365.000020 nutritional status or endocrine disorders. The method may be implemented in various usage scenarios including at-home monitoring where an owner captures weekly images, tele-triage where a veterinarian invites image upload before consultation, and in-clinic quick scan for pre-vaccination checks. The method may include using silent LEDs and ensuring capture time is less than 1 second for animal comfort. The method may include implementing a cleaning protocol for the device.

[0357] According to another aspect of the present disclosure, a system for multispectral analysis of biological tissue is provided. The system includes a multispectral sensing device and a processing unit configured to execute machine learning algorithms for analyzing the captured multispectral data.

[0358] According to other aspects of the present disclosure, the system may include one or more of the following features. The system may be adaptable to various animal mucosa pigments for reference. The system may implement algorithms to detect and analyze biomarkers related to pet health. The system may generate outputs including risk scores, trend graphs, and a vet-shareable report that is FHIR compliant. The system may be configured for various usage modes including at-home monitoring, teletriage, and in-clinic quick scan.

[0359] According to another aspect of the present disclosure, the multispectral bio-diagnostic system may be utilized for recommending supplements and pharmaceuticals for pets based on the non-invasive diagnostic results. The system may analyze multispectral data captured from various pet species to identify potential health issues and suggest appropriate interventions.

[0360] For dogs, the system may analyze multispectral images of the skin, coat, and mucous membranes to detect signs of nutritional deficiencies or imbalances. Based on the spectral signatures associated with specific nutrient levels, the system may recommend tailored supplement regimens. For example, the system may detect spectral patterns indicative of low omega-3 fatty acid levels and suggest appropriate fish oil supplements.

[0361] In cats, the multispectral imaging system may be used to assess oral health and thyroid function. By capturing and analyzing spectral data from the oral cavity and neck region, the system may identify early signs of dental disease or thyroid abnormalities. This information may be used to recommend dental care products or suggest further thyroid testing and potential medication.

[0362] The system may also be adapted for use with other pet species, such as birds, rabbits, hamsters, and reptiles. For these animals, the multispectral imaging technology may be used to analyze features such as feather condition, skin texture, or shell patterns. By comparing the captured spectral data to species-specific health indicators, the system may suggest appropriate supplements or treatments tailored to the unique needs of these pets.

[0363] In some cases, the system may incorporate a database of spectral signatures associated with various health conditions and nutritional states across different pet species. This database may be Attorney Reference No. 444365.000020 continuously updated with new research findings and clinical data to improve the accuracy of supplement and pharmaceutical recommendations.

[0364] The multispectral imaging system may also be used to monitor the effectiveness of recommended interventions over time. By capturing periodic multispectral images, the system may track changes in tissue properties and biomarkers, allowing for adjustment of supplement dosages or pharmaceutical prescriptions as needed.

[0365] In some implementations, the system may integrate with veterinary electronic health record systems, allowing for seamless sharing of diagnostic results and recommendation histories between pet owners and veterinary professionals. This integration may facilitate more informed decision-making and personalized care plans for pets.

[0366] The system may also include safeguards to ensure that supplement and pharmaceutical recommendations are appropriate for the specific pet. These safeguards may include considerations for age, weight, breed, and known health conditions, as well as potential interactions between recommended products and existing medications.

[0367] According to another aspect of the present disclosure, the multispectral bio-diagnostic system may incorporate virtual microbiome sequencing capabilities that enable non-invasive assessment of a pet's microbial composition without requiring physical biological samples. This innovative approach leverages detailed spectral analysis of accessible tissues to infer microbiome characteristics that would traditionally require laboratory -based sequencing methods.

[0368] The virtual microbiome sequencing functionality utilizes the principle that microbial communities within a pet's body produce metabolic byproducts that can influence the spectral characteristics of accessible tissues. The system detects these subtle spectral changes through analysis of tongue, gum, and oral cavity tissues, which serve as indicators of broader microbial activity throughout the pet's system.

[0369] The system may also be integrated with smart water dispensers to provide continuous health monitoring during routine drinking activities, or incorporated into wearable pet collar devices that function as comprehensive activity and health monitoring systems with additional features such as AI- enhanced audio technology for analyzing pet vocalizations and environmental sensors to monitor ambient conditions affecting pet health.

[0370] By incorporating these technical details and features, the multispectral bio-diagnostic system may provide a comprehensive solution for non-invasive pet health assessment across a wide range of species and clinical scenarios.

[0371] The foregoing general description of the illustrative examples and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive. Attorney Reference No. 444365.000020

[0372] A multispectral bio-diagnostic system may be designed for non-invasive pet health assessment. This system utilizes advanced multispectral imaging technology to capture and analyze various physiological indicators in pets, providing veterinarians and pet owners with valuable insights into the animal's health status without the need for invasive procedures.

[0373] In some cases, the multispectral bio-diagnostic system may incorporate a combination of sensors and imaging devices capable of detecting and measuring a wide range of spectral data from visible light to near-infrared wavelengths. This comprehensive spectral analysis may allow for the detection of subtle changes in skin color, tissue composition, and other physiological markers that may be indicative of various health conditions in pets.

[0374] The system may be configured to capture multispectral images of different areas of a pet's body, such as the skin, eyes, or oral cavity. These images may be processed and analyzed using sophisticated algorithms to extract relevant health information. In some implementations, the system may compare the captured data against a database of known health parameters to identify potential abnormalities or early signs of disease.

[0375] One of the key aspects of this multispectral bio-diagnostic system may be its ability to provide a non-invasive means of health assessment. This approach may reduce stress on the animal and may allow for more frequent monitoring of pet health without the need for blood draws or other invasive diagnostic procedures.

[0376] In some cases, the system may be designed for use in veterinary clinics, providing veterinarians with an additional diagnostic tool to complement traditional examination methods. Additionally, simplified versions of the system may be developed for home use, enabling pet owners to perform regular health checks and potentially detect early warning signs of health issues.

[0377] The multispectral bio-diagnostic system may incorporate machine learning algorithms that can improve the accuracy of diagnoses over time as more data is collected and analyzed. This continuous learning capability may enhance the system's ability to detect subtle changes in pet health and may potentially lead to earlier detection of developing health problems.

[0378] In some implementations, the system may include a user-friendly interface that presents the diagnostic results in an easily understandable format. This interface may provide visual representations of the multispectral data, highlighting areas of concern and offering suggestions for further veterinary consultation when necessary.

[0379] The multispectral bio-diagnostic system may be adaptable to various pet species, with calibration options to account for differences in skin pigmentation, fur thickness, and other species-specific characteristics. This versatility may allow the system to be used for a wide range of pets, from cats and dogs to smaller animals like rabbits or guinea pigs. Attorney Reference No. 444365.000020

[0380] By providing a non-invasive, comprehensive approach to pet health assessment, this multispectral bio-diagnostic system may represent a significant advancement in veterinary diagnostics and preventive care for pets.

[0381] The multispectral sensing device may incorporate advanced sensor technology to capture detailed spectral data across multiple wavelengths. In some cases, the device may utilize a multispectral single-pixel detector array as its core sensing component. This array may be engineered with microscale optical filters integrated directly onto each pixel, enabling simultaneous data acquisition across various spectral bands.

[0382] The microscale optical filters may be finely tuned to isolate specific spectral bands. In some implementations, the device may be capable of capturing images at eight distinct wavelengths. This multi-band isolation capability may allow for the collection of comprehensive spectral information, which may be crucial for accurate analysis in various applications.

[0383] The multispectral sensing device may also include near-infrared (NIR) sensing capabilities. In some cases, the device may incorporate an NIR sensor centered at 910 nanometers. This specific wavelength may be selected for its ability to penetrate biological tissues to a certain depth, potentially allowing for applications such as blood flow monitoring or subcutaneous imaging.

[0384] The integration of microscale filters directly onto the pixels of the detector array may offer several potential advantages. This design may eliminate the need for separate, bulky optical components such as prisms or gratings traditionally used in multispectral imaging systems. As a result, the overall device may be more compact and potentially more robust, with fewer moving parts that could be prone to misalignment or failure.

[0385] The multispectral sensing device may incorporate a sensor mosaic with specific filter configurations to capture data across multiple spectral bands. In some implementations, the sensor array may include filters centered at the following wavelengths:

[0386] 450 ± 10 nm

[0387] 500 ± 10 nm

[0388] 550 ± 10 nm

[0389] 600 ± 10 nm

[0390] 650 ± 10 nm

[0391] 700 ± 10 nm

[0392] 750 ± 10 nm

[0393] 800 ± 10 nm

[0394] 910 ± 15 nm (NIR)

[0395] The device may utilize an adaptive illumination loop to optimize image capture conditions. This system may adjust LED intensity and spectral output based on real-time feedback from the sensor array. Attorney Reference No. 444365.000020

In some cases, the adaptive illumination may compensate for variations in ambient lighting or tissue properties to ensure consistent and accurate spectral measurements.

[0396] The machine learning pipeline implemented in the device may include multiple stages for data processing and analysis. In some implementations, this pipeline may consist of:

[0397] 1. Image pre-processing: Including noise reduction, artifact removal, and image registration

[0398] 2. Spectral reconstruction: Using deep learning models to reconstruct full spectral information from the discrete filter measurements

[0399] 3. Feature extraction: Identifying relevant spectral features and biomarkers

[0400] 4. Classification and regression: Applying trained models to compute health indices and risk scores

[0401] The multispectral bio-diagnostic system may implement algorithms to detect and analyze various biomarkers related to pet health. These may include:

[0402] Tongue pallor: Spectral signatures associated with hemoglobin concentration

[0403] Gingival color: Reflectance patterns indicative of tissue perfusion and hydration status

[0404] Facial thermal patterns: NIR-based assessment of subcutaneous blood flow and potential inflammation

[0405] Coat lustre index: Spectral characteristics correlated with nutritional status and endocrine function

[0406] Skin elasticity: Multispectral analysis of tissue deformation and recovery

[0407] The system may generate outputs in a format compliant with Fast Healthcare Interoperability Resources (FHIR) standards. This may include structured data elements for:

[0408] Patient demographics (species, breed, age, sex)

[0409] Vital signs and physiological measurements

[0410] Diagnostic assessment results

[0411] Recommended interventions and follow-up plans

[0412] In some implementations, the multispectral sensing device may be designed to work in conjunction with existing smartphone camera technology. This integration may involve algorithmic enhancements that enable a standard RGB camera on a smartphone to effectively capture multispectral data when used alongside the specialized sensor array.

[0413] The device's ability to capture data across multiple spectral bands, including visible light and near-infrared, may allow for a more comprehensive analysis of the target subject. In some cases, this multi-band approach may reveal information not visible to the naked eye or detectable with standard RGB imaging alone.

[0414] The specific configuration of the multispectral single-pixel detector array may vary depending on the intended application. In some examples, the array may consist of a grid of pixels, each equipped Attorney Reference No. 444365.000020 with its own microscale filter. The number and spectral characteristics of these filters may be tailored to suit particular use cases, potentially allowing for customization of the device for different fields of study or diagnostic purposes.

[0415] The near-infrared sensing capabilities centered at 910 nm may be particularly useful for certain biomedical applications. This wavelength may allow for imaging of structures slightly below the skin surface, potentially providing valuable data on blood flow, tissue composition, or other physiological parameters. The depth of penetration may vary depending on the specific tissue being examined and other factors such as pigmentation.

[0416] In some implementations, the multispectral sensing device may incorporate additional sensors or components to enhance its functionality. For example, it may include temperature sensors, accelerometers, or other environmental sensors to provide context for the spectral data being collected. These additional data points may aid in the interpretation of the multispectral information and potentially improve the accuracy of any subsequent analysis.

[0417] The device may also include onboard processing capabilities to perform initial data analysis or compression. This may help to reduce the amount of raw data that needs to be transmitted or stored, potentially improving the efficiency of the overall system, especially in applications where real-time analysis is desired.

[0418] In some cases, the multispectral sensing device may be designed to integrate with existing smartphone camera technology. This integration may involve both hardware and software components to enable effective capture of multispectral data using a standard smartphone camera.

[0419] The hardware integration may include a physical attachment mechanism that allows the multispectral sensing device to be securely connected to a smartphone. This attachment may be designed to align the multispectral sensor array with the smartphone's camera lens, ensuring proper optical coupling between the two components.

[0420] On the software side, algorithmic enhancements may be implemented to process and interpret the data captured by the smartphone camera when used in conjunction with the multispectral sensing device. These algorithms may be designed to compensate for the limitations of standard RGB cameras and extract meaningful multispectral information from the combined sensor data.

[0421] In some implementations, a dedicated mobile application may be developed to facilitate the integration between the multispectral sensing device and the smartphone. This application may control the data acquisition process, manage the communication between the smartphone and the multispectral sensor, and provide a user interface for operating the combined system.

[0422] The algorithmic enhancements may involve several key components. First, a calibration algorithm may be employed to account for variations in smartphone camera sensors and ensure Attorney Reference No. 444365.000020 consistent results across different device models. This calibration process may involve capturing images of known reference targets to establish a baseline for spectral interpretation.

[0423] Additionally, image processing algorithms may be utilized to extract relevant spectral information from the smartphone camera's RGB data when combined with the multispectral sensor readings. These algorithms may employ techniques such as spectral unmixing or machine learningbased approaches to reconstruct a more comprehensive spectral profile from the limited data provided by the smartphone camera.

[0424] In some cases, the integration may also involve real-time data fusion techniques to combine the information from the smartphone camera and the multispectral sensor array. This fusion process may allow for enhanced spatial resolution or improved spectral accuracy compared to using either component alone.

[0425] The software integration may also include features for data storage, analysis, and sharing. Captured multispectral data may be securely stored on the smartphone or uploaded to a cloud-based platform for further processing and analysis. This may enable users to access and interpret the multispectral data using more powerful computing resources when needed.

[0426] To ensure compatibility with a wide range of smartphone models, the integration approach may be designed with flexibility in mind. This may involve developing modular software components that can adapt to different camera specifications and operating systems, allowing the multispectral sensing capabilities to be accessible to a broader user base.

[0427] In some implementations, the integration may leverage the smartphone's additional sensors, such as the accelerometer or GPS, to provide contextual information for the multispectral data. This supplementary data may be used to improve the accuracy of spectral measurements or to provide location-based tagging for captured images.

[0428] The integration of the multispectral sensing device with smartphone cameras may also consider power management aspects. The software may be optimized to minimize battery drain while still providing high-quality multispectral data acquisition and processing capabilities.

[0429] In some cases, the multispectral sensing device may incorporate advanced color reconstruction capabilities, enabling accurate color representation across various lighting conditions. The device may utilize sophisticated algorithms and models to process the raw spectral data and generate precise color information.

[0430] The color reconstruction process may involve multiple steps and techniques to ensure high accuracy. In some implementations, a look-up table (LUT) based on a second-degree polynomial method may be employed for image color correction. This LUT approach may help compensate for variations in lighting conditions and camera settings, potentially improving the overall color accuracy of the reconstructed images. Attorney Reference No. 444365.000020

[0431] In some cases, the device may achieve over 95% accuracy in color reconstruction tasks. The accuracy may be quantified using a correlation coefficient, which may be greater than or equal to 0.99 in certain implementations. This high level of accuracy may be crucial for applications requiring precise color analysis, such as medical diagnostics or scientific research.

[0432] The multispectral sensing device may utilize a linear matrix method to predict color from spectral power distribution (SPD) readings. This approach may be based on the principle that human- perceived color can be represented by three values: red, green, and blue (RGB). The device may capture data from multiple narrow-band filters and potentially additional wide-band channels.

[0433] In some implementations, the RGB values may be represented as a linear combination of the SPD channels. For example, the red channel value may be calculated using a formula that incorporates coefficients for each SPD channel reading. This linear matrix approach may allow for efficient color prediction from the spectral data captured by the device.

[0434] The color reconstruction process may involve setting up a matrix equation that relates the RGB values to the SPD channel readings. The coefficients of this matrix may be estimated using techniques such as least squares regression, based on measurements of known color reference targets.

[0435] In some cases, the device may incorporate machine learning algorithms to further enhance color reconstruction accuracy. These algorithms may be trained on large datasets of spectral measurements and corresponding color values, potentially allowing for more robust color prediction across a wide range of lighting conditions and subject materials.

[0436] The color reconstruction capabilities of the multispectral sensing device may be particularly valuable in applications where accurate color representation is critical. For example, in medical diagnostics, precise color information may aid in the identification of subtle tissue changes or the assessment of skin conditions. In scientific research, accurate color reconstruction may enable more reliable analysis of spectral data across different experimental conditions.

[0437] The device's ability to maintain high color accuracy across varying lighting conditions may be a significant advantage in real-world applications. This capability may reduce the need for controlled lighting environments, potentially expanding the range of settings in which the device can be effectively used.

[0438] In some implementations, the color reconstruction process may include additional steps to account for device-specific characteristics or environmental factors. For example, the device may incorporate calibration procedures to adjust for variations in sensor sensitivity or to compensate for the spectral properties of different illumination sources.

[0439] The combination of the LUT-based color correction, linear matrix color prediction, and potentially machine learning enhancements may contribute to the overall high accuracy of the color Attorney Reference No. 444365.000020 reconstruction process. This multi-faceted approach may allow the multispectral sensing device to provide reliable color information across a diverse range of applications and operating conditions.

[0440] In some cases, the multispectral bio-diagnostic system may incorporate advanced machine learning techniques to enhance the accuracy and reliability of spectral data analysis. These techniques may include the use of Random Forest models and Deep Learning approaches, specifically Convolutional Neural Networks (CNNs), to process and analyze the multispectral images captured by the system.

[0441] The Random Forest model may be employed for color prediction tasks within the system. This ensemble learning method may combine multiple decision trees to generate robust predictions based on the input spectral data. In some implementations, the Random Forest model may be trained on a diverse dataset of spectral measurements and corresponding color values, allowing it to learn complex relationships between spectral signatures and perceived colors.

[0442] The Random Forest approach may offer several advantages for color prediction in the multispectral bio-diagnostic system. For instance, it may be capable of handling non-linear relationships between input features and output colors, potentially leading to more accurate predictions compared to simpler linear models. Additionally, the ensemble nature of Random Forests may provide a measure of uncertainty in the predictions, which may be valuable for assessing the reliability of the color reconstruction process.

[0443] In some cases, the system may utilize a Deep Learning model, specifically a Convolutional Neural Network (CNN), for processing and analyzing images captured by smartphone cameras. This CNN may be designed to extract relevant features from the input images and generate accurate spectral reconstructions based on the limited color information provided by standard smartphone camera sensors. [0444] The CNN architecture may be tailored to the specific requirements of multispectral image processing. For example, it may incorporate multiple convolutional layers to learn hierarchical features from the input images, followed by fully connected layers for spectral reconstruction. In some implementations, the CNN may be trained on a large dataset of paired smartphone camera images and corresponding high-resolution multispectral data, enabling it to learn the mapping between consumergrade RGB images and detailed spectral information.

[0445] The Deep Learning approach may offer several potential benefits for the multispectral biodiagnostic system. It may enable more accurate spectral reconstruction from smartphone camera images compared to traditional methods, potentially expanding the accessibility of multispectral analysis to a wider range of users. Additionally, the CNN may be capable of adapting to various smartphone camera models and lighting conditions, improving the robustness of the system in real-world applications.

[0446] In some cases, the system may combine the outputs of multiple machine learning models to further enhance the accuracy and reliability of spectral reconstruction. For instance, the predictions from Attorney Reference No. 444365.000020 the Random Forest color model and the CNN-based spectral reconstruction may be fused using ensemble techniques or additional machine learning algorithms to generate a final, refined spectral output.

[0447] The machine learning components of the multispectral bio-diagnostic system may be designed to continuously improve their performance over time. In some implementations, the system may incorporate online learning capabilities, allowing the models to adapt to new data and potentially improve their accuracy as more measurements are collected and analyzed.

[0448] To ensure the reliability of the machine learning-based spectral reconstruction, the system may implement various validation and quality control measures. These may include cross-validation techniques during model training, uncertainty quantification for predictions, and periodic re-evaluation of model performance using known reference samples.

[0449] In some cases, the machine learning models may be optimized for efficient execution on mobile devices, enabling real-time or near-real-time spectral reconstruction and analysis. This optimization may involve techniques such as model compression, quantization, or the use of specialized neural network architectures designed for mobile deployment.

[0450] The integration of advanced machine learning techniques in the multispectral bio-diagnostic system may contribute to its overall performance and versatility. By leveraging the power of Random Forest models and Deep Learning approaches, the system may be capable of extracting more accurate and reliable spectral information from a variety of input sources, potentially expanding its applicability across different diagnostic scenarios and environmental conditions.

[0451] In some cases, the multispectral bio-diagnostic system may incorporate a process for analyzing biological tissue using the multispectral imaging capabilities. This process may involve multiple steps, including data capture, processing, and output generation.

[0452] The data capture phase may utilize the multispectral sensing device to acquire spectral information from the biological tissue of interest. In some implementations, the device may capture images at multiple distinct wavelengths, potentially including visible light and near-infrared spectra. The specific wavelengths used may be selected based on their relevance to detecting particular biomarkers or tissue characteristics.

[0453] During data acquisition, the system may employ techniques to ensure consistent and high-quality spectral measurements. This may include automatic calibration procedures to account for variations in lighting conditions or sensor performance. In some cases, multiple images may be captured and averaged to reduce noise and improve signal quality.

[0454] Once the spectral data is captured, the system may proceed to the processing phase. This phase may involve the application of various algorithms, including machine learning models, to extract meaningful information from the raw spectral data. Attorney Reference No. 444365.000020

[0455] In some implementations, the processing may begin with pre-processing steps such as noise reduction, spectral normalization, or feature extraction. These steps may help to prepare the data for more advanced analysis and may improve the overall accuracy of the subsequent processing stages.

[0456] The system may then apply machine learning algorithms to analyze the pre-processed spectral data. In some cases, this may involve the use of convolutional neural networks (CNNs) specifically trained to identify patterns and features in multispectral images of biological tissue. These CNNs may be capable of detecting subtle spectral signatures associated with various tissue conditions or biomarkers.

[0457] In addition to CNNs, the system may employ other machine learning techniques such as random forest classifiers or support vector machines. These algorithms may be used in combination to improve the robustness and accuracy of the tissue analysis.

[0458] The machine learning models used in the processing phase may be trained on large datasets of multispectral images paired with known tissue conditions or diagnostic outcomes. This training process may enable the models to learn complex relationships between spectral patterns and biological indicators.

[0459] Following the machine learning analysis, the system may generate various outputs to present the results of the tissue analysis. In some implementations, this may include the calculation of risk scores associated with specific health conditions or tissue abnormalities. These risk scores may be derived from the probabilities output by the machine learning models and may provide a quantitative measure of the likelihood of certain tissue conditions.

[0460] The system may also generate trend graphs to visualize changes in tissue characteristics over time. These graphs may be particularly useful for monitoring the progression of certain conditions or the effectiveness of treatments. In some cases, the trend graphs may incorporate data from multiple analysis sessions, allowing for long-term tracking of tissue health.

[0461] In addition to risk scores and trend graphs, the system may produce detailed spectral maps of the analyzed tissue. These maps may highlight areas of interest or potential abnormalities, providing a visual representation of the spectral analysis results.

[0462] The output generation phase may also include the creation of summary reports that combine the various analysis results into a comprehensive overview of the tissue health status. These reports may be designed to be easily interpretable by healthcare professionals and may include recommendations for further diagnostic procedures or treatments based on the analysis results.

[0463] In some implementations, the biological tissue analysis process may incorporate feedback mechanisms to continuously improve the accuracy and reliability of the system. This may involve periodic retraining of the machine learning models with new data, as well as refinement of the analysis algorithms based on expert feedback and validation studies. Attorney Reference No. 444365.000020

[0464] The entire process of biological tissue analysis, from data capture to output generation, may be designed to operate efficiently and provide results in a timely manner. In some cases, the system may be capable of performing real-time or near-real-time analysis, potentially enabling immediate feedback during diagnostic procedures.

[0465] The multispectral bio-diagnostic system's tissue analysis process may be adaptable to various types of biological tissue and may be applicable across a range of medical and veterinary diagnostic scenarios. The flexibility of the machine learning approach may allow the system to be tailored to specific diagnostic needs or to incorporate new biomarkers as they are discovered.

[0466] In some cases, the multispectral bio-diagnostic system may be configured to detect and analyze various biomarkers associated with pet health. These biomarkers may include tongue pallor, gingival color, facial thermal patterns, and coat lustre index. The system may utilize its multispectral imaging capabilities to capture and process data related to these biomarkers, potentially providing valuable insights into the health status of pets.

[0467] The detection of tongue pallor may involve capturing multispectral images of a pet's tongue and analyzing the spectral characteristics of the tissue. In some implementations, the system may compare the captured spectral data to reference values associated with healthy tongue tissue. Deviations from these reference values may be indicative of certain health conditions, such as anemia or poor circulation. [0468] Gingival color analysis may be performed by capturing multispectral images of a pet's gums and examining the spectral reflectance patterns. The system may be calibrated to detect subtle variations in gingival color that may be associated with different health states. For example, pale gums may suggest anemia, while reddened gums may indicate inflammation or infection.

[0469] Facial thermal patterns may be assessed using the near-infrared sensing capabilities of the multispectral bio-diagnostic system. In some cases, the system may capture thermal images of a pet's face and analyze the temperature distribution patterns. These patterns may provide information about the pet's circulatory system, potential areas of inflammation, or other physiological processes that affect surface temperature.

[0470] The coat lustre index may be evaluated by capturing multispectral images of a pet's fur and analyzing the spectral reflectance properties. The system may be designed to quantify the glossiness or sheen of the coat, which may be indicative of overall health and nutritional status. In some implementations, the coat lustre index may be calculated based on a combination of spectral features extracted from the multispectral images.

[0471] The multispectral bio-diagnostic system may employ machine learning algorithms to analyze the captured biomarker data and identify potential correlations with specific health conditions. These algorithms may be trained on large datasets of multispectral images paired with known health outcomes, enabling the system to recognize patterns associated with various pet health issues. Attorney Reference No. 444365.000020

[0472] In some cases, the system may generate composite health scores based on the analysis of multiple biomarkers. These scores may provide a more comprehensive assessment of a pet's overall health status, taking into account the interrelationships between different physiological indicators.

[0473] The biomarker detection and analysis capabilities of the multispectral bio-diagnostic system may be adaptable to different pet species. In some implementations, the system may include species-specific calibration settings to account for variations in normal biomarker ranges across different types of animals.

[0474] The system may also be designed to track changes in biomarker measurements over time. This longitudinal analysis may enable the detection of trends or subtle shifts in a pet's health status, potentially allowing for early identification of developing health issues.

[0475] In some cases, the biomarker analysis results may be presented in a user-friendly format, such as graphical representations or color-coded indicators. This presentation may facilitate easier interpretation of the results by veterinarians or pet owners, potentially improving the accessibility of the health information provided by the system.

[0476] The multispectral bio-diagnostic system may incorporate safeguards to ensure the reliability of biomarker measurements. These safeguards may include automatic calibration procedures, quality control checks, and error detection algorithms to identify potential issues with data acquisition or analysis.

[0477] In some implementations, the system may allow for the integration of biomarker data with other health information, such as medical history or laboratory test results. This integration may provide a more comprehensive view of a pet's health status and may assist veterinarians in making more informed diagnostic and treatment decisions.

[0478] The biomarker detection and analysis capabilities of the multispectral bio-diagnostic system may be continuously refined and expanded through ongoing research and development. In some cases, the system may be updated to incorporate new biomarkers or improved analysis techniques as they are validated through scientific studies.

[0479] In some cases, the multispectral bio-diagnostic system may be utilized in various scenarios, including at-home monitoring, tele-triage, and in-clinic quick scans. These different usage scenarios may allow for flexible and comprehensive pet health assessment across different environments and user needs.

[0480] For at-home monitoring, the system may be designed for use by pet owners in their own homes. In some implementations, the multispectral sensing device may be configured as a compact, portable unit that can be easily operated by individuals without specialized training. The device may connect wirelessly to a smartphone or tablet, allowing pet owners to capture multispectral images of their pets at regular intervals. Attorney Reference No. 444365.000020

[0481] The at-home monitoring scenario may involve a user-friendly mobile application that guides pet owners through the image capture process. This application may provide instructions on proper positioning of the pet and the device to ensure consistent and accurate measurements. In some cases, the application may include features such as reminders for scheduled health checks and the ability to track changes in biomarker measurements over time.

[0482] Data collected during at-home monitoring sessions may be securely transmitted to a cloud-based platform for analysis. The system may employ advanced encryption protocols to protect the privacy and security of pet health data during transmission and storage. In some implementations, the analysis results may be made available to pet owners through the mobile application, potentially providing insights into their pet's health status and trends over time.

[0483] The tele-triage scenario may leverage the multispectral bio-diagnostic system to facilitate remote veterinary consultations. In this usage scenario, pet owners may capture multispectral images of their pets using the at-home monitoring setup. The system may then process these images and generate a preliminary health assessment based on the detected biomarkers.

[0484] In some cases, the tele-triage functionality may include a secure communication platform that allows pet owners to share the multispectral analysis results with veterinarians. This platform may enable veterinarians to review the data remotely and provide initial guidance on whether an in-person examination may be necessary. The system may incorporate features such as video conferencing or textbased chat to facilitate direct communication between pet owners and veterinarians during the tele-triage process.

[0485] For in-clinic quick scans, the multispectral bio-diagnostic system may be adapted for use in veterinary clinics or hospitals. In this scenario, the system may be designed for rapid health assessments as part of routine check-ups or during emergency visits. The in-clinic version of the system may include additional features or capabilities compared to the at-home monitoring device, potentially allowing for more comprehensive data collection and analysis.

[0486] In some implementations, the in-clinic system may be integrated with existing veterinary practice management software, enabling seamless incorporation of multispectral analysis results into patient records. The system may include a high-throughput mode for efficiently processing multiple pets during busy clinic hours, potentially reducing wait times and improving overall clinic efficiency.

[0487] The implementation of the multispectral bio-diagnostic system across these different usage scenarios may involve various technical considerations. For data management, the system may employ a centralized database architecture that allows for secure storage and retrieval of pet health data across different usage contexts. This architecture may enable continuity of care by allowing veterinarians to access historical multispectral data regardless of where it was captured. Attorney Reference No. 444365.000020

[0488] In some cases, the system may utilize edge computing techniques to perform initial data processing on the multispectral sensing device itself. This approach may reduce the amount of raw data that needs to be transmitted, potentially improving system responsiveness and reducing bandwidth requirements, especially in scenarios with limited internet connectivity.

[0489] The multispectral bio-diagnostic system may incorporate adaptive machine learning algorithms that can refine their analysis based on the specific usage scenario. For example, the algorithms may adjust their sensitivity or specificity thresholds based on whether the analysis is being performed for at- home monitoring or in-clinic assessment. This adaptability may help optimize the system's performance across different usage contexts.

[0490] In some implementations, the system may include a feedback mechanism that allows veterinarians to provide input on the accuracy of the multispectral analysis results. This feedback may be used to continuously improve the system's performance and adapt to new or emerging health conditions.

[0491] The multispectral bio-diagnostic system may also incorporate interoperability features to facilitate data exchange with other pet health management systems. In some cases, this may involve the use of standardized data formats and communication protocols to ensure compatibility across different platforms and devices.

[0492] For user interaction, the system may employ a modular interface design that can be tailored to different usage scenarios. In the at-home monitoring context, the interface may prioritize simplicity and ease of use for pet owners. In contrast, the in-clinic interface may offer more advanced features and detailed data visualization options for veterinary professionals.

[0493] In some implementations, the system may include safeguards to prevent misuse or misinterpretation of the multispectral analysis results. This may involve clear communication of the system's limitations and the importance of professional veterinary assessment in conjunction with the multispectral data.

[0494] The multispectral bio-diagnostic system may be designed with scalability in mind, allowing for future expansion of its capabilities and adaptation to new usage scenarios as they emerge. This scalability may be achieved through modular software architecture and hardware designs that can accommodate additional sensors or analysis modules.

[0495] In some cases, the multispectral bio-diagnostic system may incorporate a physical model of the spectral power distribution (SPD) sensor for spectrum reconstruction. This physical model may be designed to accurately represent the behavior and characteristics of the SPD sensor, enabling more precise interpretation of the captured spectral data.

[0496] The physical model of the SPD sensor may be based on the specific optical and electronic properties of the sensor components. In some implementations, the model may account for the spectral Attorney Reference No. 444365.000020 response curves of individual filter channels within the sensor array. These response curves may be characterized by their center wavelengths, bandwidths, and peak transmittance values.

[0497] The spectrum reconstruction process using the physical model may involve several steps. Initially, the raw sensor readings from each channel may be processed to account for factors such as dark current and gain variations. In some cases, this preprocessing may include temperature compensation to ensure consistent performance across different operating conditions.

[0498] Following the initial preprocessing, the physical model may apply a set of mathematical transformations to convert the filtered sensor readings into a continuous spectral representation. These transformations may be derived from the known optical properties of the sensor's filter array and may incorporate techniques such as spectral interpolation or matrix inversion.

[0499] In some implementations, the physical model may include provisions for handling spectral overlap between adjacent filter channels. This may involve the use of deconvolution algorithms to separate the contributions of different wavelengths to each channel's output.

[0500] The physical model may also account for the sensor's response to different illumination conditions. In some cases, this may include modeling the impact of various light sources on the captured spectra, potentially enabling more accurate reconstruction under diverse lighting environments.

[0501] To enhance the accuracy of the spectrum reconstruction, the physical model may incorporate calibration data obtained from measurements of known reference standards. This calibration process may help compensate for manufacturing variations in individual sensors and may improve the consistency of spectral reconstructions across different devices.

[0502] In some cases, the physical model may be implemented as part of the system's onboard processing capabilities, enabling real-time or near-real-time spectrum reconstruction. Alternatively, the model may be executed on external computing resources, such as cloud-based servers, to leverage greater computational power for more complex reconstruction algorithms.

[0503] The spectrum reconstruction capabilities enabled by the physical model may have various applications within the multispectral bio-diagnostic system. For instance, the reconstructed spectra may be used as inputs for machine learning algorithms designed to identify specific biomarkers or tissue characteristics. The high-fidelity spectral data provided by the model may potentially improve the accuracy and reliability of these diagnostic algorithms.

[0504] In some implementations, the physical model may be designed to adapt and improve over time based on accumulated measurement data. This adaptive approach may involve periodic refinement of the model parameters to account for long-term changes in sensor characteristics or to incorporate new insights into the sensor's behavior under different conditions.

[0505] The physical model of the SPD sensor may also facilitate the development of simulation tools for the multispectral bio-diagnostic system. These tools may allow researchers and developers to predict Attorney Reference No. 444365.000020 system performance under various scenarios, potentially accelerating the development of new diagnostic algorithms or sensor configurations.

[0506] In some cases, the spectrum reconstruction capabilities provided by the physical model may enable the system to generate standardized spectral outputs. This standardization may facilitate easier comparison of results across different devices or measurement sessions, potentially enhancing the reproducibility of diagnostic assessments.

[0507] The physical model may also play a role in the system's quality control processes. By comparing actual sensor outputs to the expected responses predicted by the model, the system may be able to detect potential sensor malfunctions or calibration drift, prompting maintenance or recalibration as needed.

[0508] In some implementations, the physical model may be extended to account for the optical properties of biological tissues. This extension may enable more accurate interpretation of the captured spectra in the context of specific diagnostic applications, potentially improving the system's ability to detect subtle spectral signatures associated with various health conditions.

[0509] The spectrum reconstruction capabilities enabled by the physical model may contribute to the overall flexibility and adaptability of the multispectral bio-diagnostic system. By providing a robust foundation for interpreting raw sensor data, the model may facilitate the development of new diagnostic applications and the incorporation of additional spectral bands in future sensor designs.

[0510] In some cases, the multispectral bio-diagnostic system may be designed with adaptability features to accommodate various animal mucosa pigments and different pet species. This adaptability may be crucial for ensuring accurate and reliable diagnostic results across a diverse range of animals.

[0511] The system may incorporate adjustable spectral sensitivity settings to account for variations in mucosa pigmentation among different pet species. In some implementations, these settings may be automatically calibrated based on the detected species or manually adjusted by the user. The spectral sensitivity adjustments may help optimize the system's ability to detect subtle color changes and tissue characteristics across animals with varying natural pigmentation levels.

[0512] Species-specific algorithms may be employed within the multispectral bio-diagnostic system to enhance its analytical capabilities. These algorithms may be developed using machine learning techniques trained on large datasets of multispectral images from various pet species. In some cases, the system may utilize separate neural network models for different animal categories, such as canines, felines, and small mammals, to account for the unique physiological characteristics of each group.

[0513] The system may maintain a comprehensive database of reference spectral data for different pet species. This database may include normative ranges for various biomarkers and tissue characteristics specific to each species. In some implementations, the reference data may be regularly updated to incorporate new findings from veterinary research, ensuring that the system's diagnostic capabilities remain current and accurate. Attorney Reference No. 444365.000020

[0514] To facilitate species-specific analysis, the multispectral bio-diagnostic system may include a species identification module. This module may use computer vision techniques to automatically recognize the type of animal being examined based on the captured images. The species identification may then trigger the appropriate analytical algorithms and reference data sets for that particular animal type.

[0515] In some cases, the system may allow for manual input of species information to ensure accurate analysis when automatic identification may be challenging. This feature may be particularly useful for exotic pets or rare breeds that may not be well-represented in the system's training data.

[0516] The adaptability of the multispectral bio-diagnostic system may extend to accounting for age- related changes in animal physiology. In some implementations, the system may adjust its analytical parameters based on the age of the pet, as certain biomarkers and tissue characteristics may vary throughout an animal's lifespan.

[0517] To address variations in coat color and thickness among different pet species, the system may incorporate adaptive image processing techniques. These techniques may help isolate and analyze the relevant mucosa areas even in animals with dark or thick fur around the mouth and eyes. In some cases, the system may provide guidance to users on proper fur parting or grooming to ensure optimal image capture.

[0518] The multispectral bio-diagnostic system may also consider breed-specific variations within a given species. In some implementations, the system may maintain separate reference data sets for different breeds of dogs or cats, accounting for known physiological differences that may impact diagnostic assessments.

[0519] To enhance its adaptability, the system may employ transfer learning techniques in its machine learning models. This approach may allow the system to leverage knowledge gained from analyzing one species to improve its performance on related species, potentially enabling more accurate diagnostics for less common pet types.

[0520] In some cases, the multispectral bio-diagnostic system may include a feedback mechanism that allows veterinarians to provide input on the accuracy of species-specific analyses. This feedback may be used to continuously refine and improve the system's algorithms and reference data, adapting to new insights in veterinary medicine.

[0521] The system may also incorporate environmental factors into its species-specific considerations. In some implementations, the analytical algorithms may account for regional variations in pet health, such as prevalent parasites or environmental stressors that may affect certain biomarkers differently across geographic locations.

[0522] To ensure comprehensive adaptability, the multispectral bio-diagnostic system may allow for the integration of custom reference data sets. This feature may enable veterinary specialists to Attorney Reference No. 444365.000020 incorporate their own research findings or clinical data into the system, potentially improving its diagnostic capabilities for specific animal populations or health conditions.

[0523] In some cases, the system may employ multi-modal analysis techniques that combine multispectral imaging with other diagnostic modalities. This approach may help corroborate findings and improve diagnostic accuracy across different pet species, particularly in cases where spectral analysis alone may be insufficient.

[0524] The adaptability features of the multispectral bio-diagnostic system may be designed with scalability in mind. In some implementations, the system architecture may allow for easy updates and expansions to include new species or breeds as needed, ensuring that the system remains versatile and relevant in diverse veterinary practice settings.

[0525] In some cases, the multispectral bio-diagnostic system may incorporate functionality to generate supplement and pharmaceutical recommendations based on the non-invasive diagnostic results obtained from the multispectral analysis. This feature may provide veterinarians and pet owners with guidance on potential interventions tailored to the specific health status of individual animals.

[0526] The system may utilize a comprehensive database of supplement and pharmaceutical information, including dosage guidelines, contraindications, and known efficacy for various health conditions across different pet species. This database may be regularly updated to reflect the latest veterinary research and clinical findings.

[0527] In some implementations, the system may employ machine learning algorithms to analyze the multispectral data and correlate specific spectral signatures with known health conditions. These algorithms may be trained on large datasets of multispectral images paired with confirmed diagnoses and successful treatment outcomes.

[0528] The recommendation generation process may involve several steps. Initially, the system may categorize the detected biomarkers and spectral patterns into broad health categories, such as nutritional deficiencies, inflammatory conditions, or metabolic imbalances. This categorization may serve as a starting point for more detailed analysis.

[0529] Following the initial categorization, the system may apply species-specific analytical models to refine the interpretation of the multispectral data. These models may account for variations in normal physiological parameters across different animal types, potentially improving the accuracy of the generated recommendations.

[0530] In some cases, the system may incorporate a rule-based expert system to translate the analyzed spectral data into specific supplement or pharmaceutical suggestions. This expert system may be designed to mimic the decision-making process of experienced veterinarians, taking into account factors such as the severity of detected abnormalities, the animal's age and breed, and potential interactions between different interventions. Attorney Reference No. 444365.000020

[0531] The multispectral bio-diagnostic system may generate recommendations in a hierarchical format, prioritizing interventions based on their potential impact and urgency. For example, the system may distinguish between immediate pharmaceutical interventions for acute conditions and long-term supplementation strategies for chronic health management.

[0532] In some implementations, the system may provide dosage recommendations for suggested supplements or pharmaceuticals. These dosage suggestions may be calculated based on the animal's weight, age, and the severity of the detected condition. The system may also include warnings about potential side effects or contraindications associated with specific recommendations.

[0533] The recommendation generation process may incorporate a temporal analysis component, comparing current multispectral data with historical measurements for the same animal when available. This longitudinal analysis may enable the system to detect trends or changes in health status over time, potentially influencing the nature and urgency of the generated recommendations.

[0534] In some cases, the system may offer alternative intervention options for each identified health issue. This feature may provide veterinarians with flexibility in treatment planning, allowing them to consider factors such as cost, availability, and individual pet owner preferences when selecting interventions.

[0535] The multispectral bio-diagnostic system may include a mechanism for veterinarians to review and approve the generated recommendations before they are presented to pet owners. This review process may serve as a quality control measure, ensuring that the automated suggestions align with professional clinical judgment.

[0536] In some implementations, the system may generate explanatory notes alongside each recommendation, detailing the rationale behind the suggestion and how it relates to the detected spectral abnormalities. These explanations may help veterinarians and pet owners understand the basis for the recommendations and make informed decisions about treatment options.

[0537] The system may also incorporate feedback mechanisms to track the outcomes of implemented recommendations. In some cases, this feedback may be used to refine and improve the recommendation algorithms over time, potentially enhancing the accuracy and effectiveness of future suggestions.

[0538] For complex cases or situations where multiple health issues are detected, the multispectral biodiagnostic system may employ optimization algorithms to generate comprehensive treatment plans. These algorithms may consider potential interactions between different interventions and aim to maximize overall health benefits while minimizing risks and side effects.

[0539] In some cases, the system may integrate with external databases of drug interactions and contraindications to ensure the safety of generated recommendations. This integration may help identify potential conflicts between suggested pharmaceuticals and any existing medications the animal may be taking. Attorney Reference No. 444365.000020

[0540] The multispectral bio-diagnostic system may include features for customizing recommendation parameters based on individual veterinary practices or regional guidelines. This customization may allow for the incorporation of local expertise and preferences into the recommendation generation process.

[0541] In some implementations, the system may generate visual representations of expected treatment outcomes based on historical data and known efficacy rates. These visualizations may help veterinarians communicate potential benefits and risks of recommended interventions to pet owners.

[0542] The recommendation generation functionality may be designed with scalability in mind, allowing for the incorporation of new treatment options and intervention strategies as they become available. This adaptability may help ensure that the system remains current and relevant in the face of evolving veterinary medicine practices.

[0543] In some cases, the multispectral bio-diagnostic system may be designed to integrate seamlessly with existing veterinary electronic health record (EHR) systems. This integration may facilitate efficient data sharing and management between the diagnostic device and veterinary practice management software.

[0544] The system may incorporate standardized data exchange protocols to ensure compatibility with a wide range of veterinary EHR platforms. In some implementations, the multispectral bio-diagnostic system may utilize Fast Healthcare Interoperability Resources (FHIR) standards for generating and transmitting diagnostic reports. FHIR-compliant reports may enable structured, standardized communication of health information across different systems and healthcare providers.

[0545] The multispectral bio-diagnostic system may include a dedicated module for generating FHIR- compliant reports based on the analysis of spectral data. These reports may contain detailed information about the detected biomarkers, spectral abnormalities, and potential health indicators identified during the diagnostic process. In some cases, the FHIR reports may also include metadata such as timestamp, device identification, and operator information to ensure traceability and data integrity.

[0546] To facilitate integration with veterinary EHR systems, the multispectral bio-diagnostic device may employ secure application programming interfaces (APIs). These APIs may allow for bidirectional data exchange, enabling the device to retrieve relevant patient history from the EHR system and push newly generated diagnostic data back into the patient's electronic record.

[0547] In some implementations, the FHIR-compliant output may facilitate integration with veterinary electronic health record systems and enable seamless data exchange between different healthcare providers.

[0548] Preferred Settings:

[0549] The multispectral bio-diagnostic system may operate optimally under specific conditions. In some cases, the preferred settings may include: Attorney Reference No. 444365.000020

[0550] LED intensity: 50-100 mW/cm² per spectral band, adjustable based on tissue type and ambient light

[0551] Capture time: < 500 ms total for all spectral bands

[0552] Device-to-subject distance: 5-10 cm, maintained by an adjustable spacer

[0553] Ambient light: < 500 lux to minimize interference with controlled illumination

[0554] Temperature range: 10-35°C for consistent sensor performance

[0555] These settings may be automatically adjusted by the device's adaptive control system to maintain optimal performance across various usage scenarios.

[0556] The multispectral bio-diagnostic system may incorporate safeguards to ensure the accuracy and reliability of its measurements and recommendations. In some implementations, these safeguards may include:

[0557] Self-calibration routines performed at regular intervals or before each use

[0558] Real-time quality checks on captured images to detect motion artifacts or improper positioning

[0559] Confidence scores associated with each biomarker measurement and health assessment

[0560] User guidance for proper device positioning and image capture techniques

[0561] The system may also include features to enhance its usability and integration into veterinary workflows. These may include:

[0562] Voice-activated controls for hands-free operation during examinations

[0563] Augmented reality overlays to guide proper device positioning on the animal

[0564] Cloud-based data storage and analysis for longitudinal health tracking

[0565] Integration with veterinary practice management software for streamlined record-keeping and billing

[0566] In some implementations, the system may incorporate role-based access controls to manage data sharing between pet owners and veterinary professionals. This feature may allow pet owners to grant specific permissions to their veterinarians, enabling controlled access to the multispectral diagnostic data and historical trends.

[0567] The data sharing process between pet owners and veterinary professionals may involve a secure cloud-based platform. This platform may serve as an intermediary, storing encrypted diagnostic data and managing access permissions. In some cases, pet owners may be able to view simplified versions of their pet's diagnostic reports through a dedicated mobile application, while veterinarians may have access to more detailed clinical data through their professional interfaces.

[0568] To enhance collaboration between pet owners and veterinarians, the system may include features for annotating and commenting on diagnostic results. This functionality may allow veterinarians to provide context or explanations for specific findings, potentially improving communication and pet owner understanding of their animal's health status. Attorney Reference No. 444365.000020

[0569] In some cases, the multispectral bio-diagnostic system may support the creation of longitudinal health records by aggregating data from multiple diagnostic sessions over time. This historical data may be integrated into the veterinary EHR system, allowing for trend analysis and long-term health monitoring of individual pets.

[0570] The system may incorporate data validation mechanisms to ensure the accuracy and consistency of information shared between the multispectral bio-diagnostic device and veterinary EHR systems. These mechanisms may include checksums, data format verification, and automatic error detection to minimize the risk of data corruption or misinterpretation during the integration process.

[0571] To accommodate varying levels of technological infrastructure across veterinary practices, the multispectral bio-diagnostic system may offer flexible integration options. In some implementations, the system may support both direct integration with on-premises EHR systems and cloud-based synchronization for practices utilizing web-based management software.

[0572] The integration capabilities of the multispectral bio-diagnostic system may extend to supporting telemedicine applications. In some cases, the system may facilitate secure sharing of diagnostic data and FHIR reports with remote veterinary specialists, enabling collaborative diagnosis and treatment planning for complex cases.

[0573] To ensure compliance with data protection regulations, the multispectral bio-diagnostic system may incorporate robust encryption protocols for data storage and transmission. These security measures may help protect sensitive pet health information throughout the integration and data sharing processes. [0574] In some implementations, the system may include audit trail functionality to track all data access and modifications. This feature may provide a comprehensive record of interactions with the diagnostic data, potentially enhancing accountability and supporting regulatory compliance efforts in veterinary practices.

[0575] The multispectral bio-diagnostic system may offer customizable data integration workflows to accommodate the specific needs of different veterinary practices. These workflows may allow practices to define how diagnostic data is incorporated into their existing patient management processes, potentially streamlining operations and improving overall efficiency.

[0576] To facilitate seamless integration with veterinary EHR systems, the multispectral bio-diagnostic device may support automatic software updates. These updates may ensure ongoing compatibility with evolving EHR standards and introduce new integration features or improvements over time.

[0577] In some cases, the system may provide tools for data migration and historical record import. These tools may allow veterinary practices to incorporate previously collected health data into the multispectral bio-diagnostic system, potentially enhancing the comprehensiveness of pet health profiles and supporting more informed diagnostic assessments. Attorney Reference No. 444365.000020

[0578] The multispectral bio-diagnostic system may include features for generating customized reports tailored to specific veterinary specialties or practice types. These specialty-specific reports may present diagnostic data in formats optimized for particular areas of veterinary medicine, potentially enhancing the system's utility across diverse clinical settings.

[0579] To support research and quality improvement initiatives, the system may offer anonymized data aggregation capabilities. In some implementations, veterinary practices may opt to contribute de- identified diagnostic data to a centralized research database, potentially advancing understanding of pet health trends and supporting evidence-based veterinary medicine.

[0580] The integration features of the multispectral bio-diagnostic system may be designed with scalability in mind, allowing for future expansion to accommodate new data types, diagnostic modalities, or emerging veterinary health management paradigms. This adaptability may help ensure the long-term relevance and utility of the system within evolving veterinary practice environments.

[0581] In some cases, the multispectral bio-diagnostic system may incorporate various safety and comfort features to ensure a positive experience for both pets and their owners during the diagnostic process. These features may be designed to minimize stress and discomfort for the animal while maintaining the accuracy and reliability of the diagnostic measurements.

[0582] The system may utilize silent light-emitting diodes (LEDs) as illumination sources for the multispectral imaging process. These LEDs may be selected for their ability to produce the necessary spectral output without generating audible noise that could potentially startle or disturb the pet. The silent operation of the LEDs may help create a calm environment during the diagnostic procedure, potentially reducing anxiety in nervous or sensitive animals.

[0583] In some implementations, the multispectral bio-diagnostic system may employ a rapid capture time for image acquisition. This quick imaging process may minimize the duration that the pet needs to remain still, potentially reducing stress and improving the overall comfort of the experience. The rapid capture capability may be particularly beneficial for restless animals or those with limited attention spans.

[0584] The system may incorporate automatic exposure control mechanisms to optimize image quality while minimizing the intensity and duration of light exposure. These mechanisms may adjust the LED output and capture settings based on the specific characteristics of each pet, such as coat color or skin pigmentation. By tailoring the illumination to individual animals, the system may help ensure comfort while maintaining diagnostic accuracy.

[0585] In some cases, the multispectral bio-diagnostic device may feature a ergonomic design with smooth, rounded edges to prevent any accidental injury to pets or handlers during use. The device housing may be constructed from pet-safe materials that are resistant to scratching or chewing, reducing the risk of damage to the device or harm to the animal. Attorney Reference No. 444365.000020

[0586] The system may include a cleaning protocol to maintain hygiene between uses. This protocol may involve the use of pet-safe, non-toxic disinfectants that effectively sanitize the device without leaving residues that could irritate an animal's skin or mucous membranes. In some implementations, the device may feature removable, washable covers for components that come into direct contact with pets, allowing for easy cleaning and replacement.

[0587] To enhance pet comfort during the diagnostic process, the system may incorporate temperature regulation features. These features may help ensure that any components that come into contact with the animal remain at a comfortable temperature, potentially preventing startling sensations from cold surfaces or discomfort from excessive warmth.

[0588] In some cases, the multispectral bio-diagnostic system may include adjustable positioning aids to accommodate pets of various sizes and temperaments. These aids may help gently secure the animal in the optimal position for imaging while minimizing physical restraint. The positioning system may be designed to be quickly adjustable, allowing for rapid setup and reducing the time the pet needs to remain in a specific posture.

[0589] The system may offer customizable comfort settings that can be tailored to individual pets based on their known preferences or sensitivities. These settings may include adjustable light intensity, capture speed, or even the option to use calming sounds or vibrations to distract and soothe anxious animals during the diagnostic process.

[0590] In some implementations, the multispectral bio-diagnostic device may feature a "pet-friendly" mode that gradually introduces the animal to the diagnostic process. This mode may involve a series of low-intensity light pulses or gentle movements to acclimate the pet to the device before proceeding with the full diagnostic scan. This gradual introduction may help reduce anxiety and improve cooperation, especially for pets that are new to the procedure.

[0591] The system may incorporate sensors to monitor the pet's stress levels during the diagnostic process. These sensors may detect physiological indicators such as heart rate or muscle tension, potentially allowing the system to adapt its operation in real-time to minimize discomfort. In some cases, the system may automatically pause or adjust its settings if signs of significant stress are detected, prioritizing the animal's well-being.

[0592] To further enhance safety, the multispectral bio-diagnostic system may include automatic shutoff features that activate if the device detects sudden movements or if the pet attempts to chew or scratch the equipment. These safety measures may help prevent potential injuries and protect both the animal and the device from damage.

[0593] In some cases, the system may offer a simulation mode that allows pet owners to familiarize their animals with the diagnostic process using non-functional replicas or virtual representations of the Attorney Reference No. 444365.000020 device. This pre-exposure may help reduce anxiety during actual diagnostic sessions by making the experience more familiar and less intimidating for the pet.

[0594] The multispectral bio-diagnostic system may provide guidance to pet owners on how to prepare their animals for the diagnostic procedure. This guidance may include recommendations for acclimating pets to handling and positioning, as well as suggestions for creating a calm environment during the diagnostic session. By educating pet owners on best practices, the system may contribute to a more positive and stress-free experience for both the animal and the handler.

[0595] In some cases, the multispectral bio-diagnostic system may offer several advantages and benefits compared to traditional diagnostic methods for pet health assessment. The non-invasive nature of the system may allow for frequent monitoring of pet health without causing stress or discomfort to the animal. This approach may be particularly beneficial for pets that are anxious or difficult to handle during conventional veterinary examinations.

[0596] The accessibility of the multispectral bio-diagnostic system may enable pet owners to perform preliminary health assessments at home. This capability may lead to earlier detection of potential health issues, potentially improving overall pet health outcomes. The system's ability to capture and analyze multispectral data may provide a more comprehensive view of pet health compared to visual inspection alone.

[0597] In some implementations, the system may utilize a CIE color-matching function for color reconstruction from spectral data. This approach may enable more accurate representation of colors observed in pet tissues, potentially enhancing the diagnostic value of the captured images. The conversion of color data to the sRGB color space may facilitate consistent display of results across various devices, improving communication between pet owners and veterinary professionals.

[0598] The multispectral bio-diagnostic system may employ advanced image processing techniques to enhance the quality and relevance of captured data. In some cases, the system may selectively analyze only the top 20% of pixels in terms of signal quality or relevance. This selective approach may help reduce noise and focus the analysis on the most informative regions of the captured images, potentially improving the accuracy and reliability of diagnostic assessments.

[0599] Compared to traditional diagnostic methods that may require blood draws or tissue samples, the non-invasive nature of the multispectral bio-diagnostic system may reduce the risk of complications and minimize stress for pets. This approach may be particularly valuable for monitoring chronic conditions or assessing the health of elderly or fragile animals that may be more susceptible to the risks associated with invasive procedures.

[0600] The multispectral bio-diagnostic system may offer the potential for more frequent health assessments without incurring significant additional costs or requiring repeated visits to veterinary clinics. This increased monitoring frequency may enable the detection of subtle changes in pet health Attorney Reference No. 444365.000020 over time, potentially allowing for earlier intervention and more effective management of developing health issues.

[0601] In some cases, the system's ability to capture and analyze multispectral data may reveal information not visible to the naked eye or detectable through conventional physical examinations. This enhanced diagnostic capability may contribute to more accurate and comprehensive health assessments, potentially leading to improved treatment outcomes and better overall pet health management.

[0602] The multispectral bio-diagnostic system may support remote consultation capabilities, allowing pet owners to share diagnostic data with veterinarians without the need for in-person visits. This feature may improve access to veterinary expertise, particularly for pet owners in remote areas or those with limited mobility.

[0603] The system's potential for generating standardized, quantitative health data may facilitate more objective tracking of pet health over time. This standardization may enable more accurate comparisons between different assessment sessions and potentially improve the consistency of diagnoses across different veterinary practitioners.

[0604] In some implementations, the multispectral bio-diagnostic system may integrate with existing veterinary electronic health record systems, potentially streamlining the process of recording and analyzing pet health data. This integration may contribute to more comprehensive and easily accessible pet health histories, potentially supporting more informed decision-making in veterinary care.

[0605] The non-invasive nature of the multispectral bio-diagnostic system may allow for more frequent assessments of pet health without causing cumulative stress or discomfort. This may be particularly beneficial for pets with chronic conditions that require ongoing monitoring, as it may reduce the need for repeated invasive procedures or stressful veterinary visits.

[0606] In some cases, the system's ability to capture and analyze multispectral data may provide early indicators of health issues before they become clinically apparent through traditional diagnostic methods. This early detection capability may enable more proactive and preventive approaches to pet health care, potentially improving long-term health outcomes and reducing the overall cost of veterinary care.

[0607] The multispectral bio-diagnostic system may be integrated with smart water dispensers to provide continuous health monitoring during routine drinking activities. In some implementations, the water dispenser may include embedded multispectral imaging sensors positioned to capture spectral data from the pet's tongue, gums, and oral cavity as the animal drinks. This integration may enable passive health monitoring without requiring specific positioning or handling of the pet.

[0608] The smart water dispenser may incorporate the same multispectral sensing technology described herein, including the sensor array with at least eight visible light filters and near-infrared capabilities. In some cases, the dispenser may be equipped with adaptive illumination systems that activate when Attorney Reference No. 444365.000020 motion sensors detect a pet approaching the water bowl. The illumination may be designed to be nonintrusive and silent to avoid disrupting the pet's natural drinking behavior.

[0609] The water dispenser integration may allow for frequent, automated health assessments as pets typically drink water multiple times throughout the day. This frequent monitoring capability may enable the detection of gradual changes in biomarkers that might not be apparent during less frequent manual examinations. The system may track hydration-related biomarkers, gingival color changes, and tongue pallor indicators over time.

[0610] In some implementations, the smart water dispenser may include flow sensors to correlate drinking patterns with health indicators. The system may analyze relationships between water consumption rates, drinking frequency, and spectral biomarker measurements to provide comprehensive health insights. This data correlation may enhance the accuracy of health assessments by considering behavioral patterns alongside physiological indicators.

[0611] The multispectral bio-diagnostic system may also be incorporated into wearable pet collar devices that function as comprehensive activity and health monitoring systems. These collar-based implementations may provide continuous monitoring capabilities similar to fitness tracking devices used by humans. The collar may include miniaturized versions of the multispectral sensing components, adapted for continuous or periodic health measurements.

[0612] The wearable collar device may incorporate accelerometers, gyroscopes, and GPS sensors to capture detailed movement metrics including step counts, activity levels, sleep patterns, and location tracking. In some cases, the collar may analyze gait patterns, exercise intensity, and rest periods to provide comprehensive activity profiles. This movement data may be correlated with multispectral health measurements to identify relationships between activity levels and physiological indicators.

[0613] The collar device may include periodic health scanning capabilities, where the multispectral sensors activate at predetermined intervals to capture spectral data from accessible areas such as the neck region or areas where the collar contacts the pet's skin. In some implementations, the collar may include retractable or adjustable sensor components that can be positioned for optimal spectral data capture during rest periods.

[0614] The wearable device may incorporate machine learning algorithms that analyze patterns in both activity data and spectral measurements to identify potential health issues. The system may detect correlations between changes in activity patterns and spectral biomarker variations, potentially providing early warning indicators for developing health conditions.

[0615] The collar-based system may include Al-enhanced audio technology capable of detecting and analyzing pet vocalizations. In some implementations, the audio sensors may capture and process various types of pet sounds including barks, whines, purrs, and other vocalizations. The Al algorithms Attorney Reference No. 444365.000020 may be trained to recognize subtle changes in vocalization patterns that may indicate emotional stress, pain, or other health-related conditions.

[0616] The audio analysis capabilities may complement the multispectral health monitoring by providing additional behavioral and emotional health indicators. In some cases, the system may correlate changes in vocalization patterns with spectral biomarker measurements to provide more comprehensive health assessments. The audio monitoring may be particularly valuable for detecting stress-related conditions that may not be immediately apparent through spectral analysis alone.

[0617] The collar device may include audio playback capabilities that can project familiar sounds, including recorded owner voices, to provide comfort to pets during periods of separation. In some implementations, the system may automatically detect signs of separation anxiety through vocalization analysis and activity pattern changes, triggering the playback of comforting audio content. This feature may help reduce stress-related health impacts and improve overall pet well-being.

[0618] The audio comfort system may be integrated with the health monitoring capabilities to assess the effectiveness of comfort interventions. The system may monitor changes in stress indicators, both through vocalization analysis and spectral biomarker measurements, to evaluate how well the audio comfort features are working for individual pets.

[0619] The integrated collar system may include wireless connectivity capabilities to transmit collected data to smartphone applications or cloud-based platforms. This connectivity may enable real-time monitoring and analysis of both activity and health data. In some cases, the system may provide immediate alerts to pet owners when significant changes in health indicators or activity patterns are detected.

[0620] The collar device may incorporate adaptive power management systems to ensure extended battery life while maintaining continuous monitoring capabilities. The system may intelligently adjust sampling rates and sensor activation based on activity levels and detected health status to optimize power consumption while maintaining monitoring effectiveness.

[0621] In some implementations, the collar-based multispectral bio-diagnostic system may include environmental sensors to monitor ambient conditions that may affect pet health. These sensors may measure temperature, humidity, air quality, and UV exposure levels. The environmental data may be correlated with health measurements to identify potential environmental factors contributing to health changes.

[0622] The wearable system may include social features that allow pet owners to share activity and health data with veterinarians, pet sitters, or other caregivers. The system may generate comprehensive reports combining activity metrics, health indicators, and behavioral patterns to provide complete pictures of pet well-being over time. Attorney Reference No. 444365.000020

[0623] The collar device may incorporate customizable alert systems that can be tailored to individual pet needs and owner preferences. The system may learn normal patterns for each pet and provide personalized alerts when deviations from typical behavior or health indicators are detected. This personalization may help reduce false alarms while ensuring that significant health changes are promptly identified.

[0624] The integration of multispectral health monitoring with comprehensive activity tracking and audio analysis may provide unprecedented insights into pet health and well-being. This multi-modal approach may enable more accurate health assessments and earlier detection of potential issues compared to single-modality monitoring systems.

[0625] The combined system may support longitudinal health studies by continuously collecting diverse data types over extended periods. This comprehensive data collection may contribute to veterinary research and improve understanding of relationships between activity, behavior, and health in various pet species and breeds.

[0626] In some cases, the integrated collar system may include machine learning capabilities that continuously improve their accuracy and effectiveness based on accumulated data from multiple pets. The system may identify population-level health trends and patterns that can inform individual pet care recommendations and veterinary best practices.

[0627] The wearable multispectral bio-diagnostic system may be designed with modular components that can be upgraded or replaced as technology advances. This modular approach may ensure that the system remains current with evolving diagnostic capabilities and sensor technologies while maintaining compatibility with existing data and analysis platforms.

[0628] The multispectral bio-diagnostic system may incorporate virtual microbiome sequencing capabilities that enable non-invasive assessment of a pet's microbial composition without requiring physical biological samples. This innovative approach may leverage the detailed spectral analysis of accessible tissues to infer microbiome characteristics that would traditionally require laboratory-based sequencing methods.

[0629] In some implementations, the virtual microbiome sequencing functionality may utilize the principle that microbial communities within a pet's body produce metabolic byproducts that can influence the spectral characteristics of accessible tissues. The system may detect these subtle spectral changes through analysis of tongue, gum, and oral cavity tissues, which may serve as indicators of broader microbial activity throughout the pet's system.

[0630] The mechanism of action for virtual microbiome sequencing may be based on the observation that metabolic byproducts from certain microbial communities can cause detectable changes in tissue properties. In some cases, gut dysbiosis or oral microbial imbalances may result in the production of Attorney Reference No. 444365.000020 specific metabolites that enter systemic circulation and influence tissue inflammation, perfusion patterns, and cellular composition in measurable ways.

[0631] These microbially-influenced tissue changes may create unique spectral signatures that can be detected by the multispectral sensing device's array of visible light and near-infrared sensors. The system may capture subtle variations in tissue reflectance, absorption, and scattering properties that correlate with specific microbial community compositions or dysbiotic states.

[0632] In some implementations, the virtual microbiome sequencing capability may employ advanced machine learning models, such as deep neural networks, specifically trained to correlate high-resolution spectral data with microbiome composition. These models may be developed using large datasets that combine the device's multispectral measurements with ground-truth microbiome data obtained through traditional laboratory methods.

[0633] The training dataset for the machine learning models may include paired samples where pets undergo both multispectral imaging and conventional microbiome analysis through 16S rRNA sequencing or shotgun metagenomic sequencing. This comprehensive dataset may enable the development of robust correlations between spectral signatures and specific microbial taxa or functional gene profiles.

[0634] The deep learning architecture may incorporate convolutional neural networks designed to identify complex patterns within the multispectral data that correspond to specific microbial signatures. In some cases, the model may utilize attention mechanisms to focus on spectral features most relevant to microbial activity, potentially improving the accuracy of microbiome predictions.

[0635] The machine learning pipeline may include preprocessing steps to normalize spectral data across different measurement conditions and pet characteristics. The system may account for factors such as breed-specific tissue properties, age-related changes, and environmental influences that could affect the relationship between spectral signatures and microbiome composition.

[0636] In some implementations, the virtual microbiome sequencing system may generate predictive microbiome profiles that identify the likely abundance of key bacterial phyla commonly found in pet microbiomes. The system may provide estimates for major taxonomic groups such as Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria, along with confidence intervals for each prediction. [0637] The virtual microbiome output may include dysbiosis detection capabilities that flag potential microbial imbalances based on the spectral analysis. The system may identify patterns associated with common dysbiotic conditions, such as small intestinal bacterial overgrowth (SIBO), inflammatory bowel conditions, or oral microbiome disruptions that may impact overall pet health.

[0638] The predictive microbiome profile may be presented in a format similar to traditional microbiome sequencing reports, including relative abundance charts, diversify indices, and comparisons Attorney Reference No. 444365.000020 to healthy reference populations. In some cases, the system may provide species-specific reference ranges based on the pet's breed, age, and other relevant characteristics.

[0639] The virtual microbiome sequencing capability may incorporate temporal analysis features that track changes in predicted microbiome composition over time. This longitudinal monitoring may enable the detection of microbiome shifts associated with dietary changes, antibiotic treatments, or developing health conditions before they become clinically apparent.

[0640] In some implementations, the system may correlate virtual microbiome data with other health indicators captured by the multispectral analysis. The integration of microbiome predictions with biomarkers such as inflammation indicators, nutritional status markers, and immune function assessments may provide comprehensive insights into the pet's overall health status.

[0641] The virtual microbiome sequencing functionality may include quality control measures to assess the reliability of predictions. The system may provide confidence scores for microbiome estimates and flag cases where spectral data quality or pet characteristics fall outside the training dataset parameters, potentially requiring traditional microbiome testing for validation.

[0642] The machine learning models may be designed to continuously improve through federated learning approaches, where anonymized data from multiple users contributes to model refinement without compromising individual pet privacy. This continuous learning capability may enhance the accuracy and scope of virtual microbiome predictions over time.

[0643] In some cases, the virtual microbiome sequencing system may integrate with veterinary treatment planning by suggesting microbiome-targeted interventions based on the predicted microbial composition. The system may recommend specific probiotics, prebiotics, or dietary modifications tailored to address identified dysbiotic patterns.

[0644] The virtual microbiome capability may extend to monitoring treatment effectiveness by tracking predicted microbiome changes in response to interventions. This monitoring functionality may enable veterinarians and pet owners to assess whether microbiome-targeted treatments are producing the desired microbial shifts without requiring repeated laboratory testing.

[0645] The system may incorporate species-specific microbiome models that account for the natural variations in microbial communities across different pet species. The virtual sequencing capability may be calibrated for dogs, cats, and other companion animals, recognizing that healthy microbiome compositions vary significantly between species.

[0646] In some implementations, the virtual microbiome sequencing may include functional prediction capabilities that estimate the metabolic potential of the predicted microbial community. The system may provide insights into likely microbial functions such as short-chain fatty acid production, vitamin synthesis, or xenobiotic metabolism based on the inferred taxonomic composition. Attorney Reference No. 444365.000020

[0647] The virtual microbiome data may be integrated with the system's supplement and pharmaceutical recommendation engine to suggest interventions specifically targeted at microbiome optimization. The system may recommend microbiome-supporting supplements or identify potential medication interactions that could further disrupt microbial balance.

[0648] The virtual microbiome sequencing capability may support research applications by enabling large-scale microbiome studies without the logistical challenges of sample collection and laboratory processing. This functionality may contribute to advancing understanding of pet microbiome health and its relationship to various disease conditions.

[0649] In some cases, the system may provide comparative analysis features that allow pet owners to track their pet's microbiome health relative to population norms or previous measurements. This comparative capability may help identify gradual microbiome changes that might indicate developing health issues or successful interventions.

[0650] The virtual microbiome sequencing functionality may be designed with appropriate limitations and disclaimers, clearly communicating that the predictions are estimates based on spectral analysis rather than direct microbial identification. The system may recommend traditional microbiome testing for cases requiring definitive microbial characterization or when virtual predictions indicate significant dysbiosis.

[0651] The multispectral bio-diagnostic system may offer the potential for more personalized pet health care by providing detailed, individual-specific health data. This personalized approach may enable veterinarians to tailor treatment plans and preventive strategies to the unique needs of each pet, potentially improving the effectiveness of interventions and enhancing overall quality of life for pets. [0652] In some implementations, the multispectral bio-diagnostic system may be configured to detect spectral signatures associated with feline leukemia virus (FeLV) infection through analysis of oral and ocular tissues. The system may identify characteristic changes in tissue oxygenation, perfusion patterns, and inflammatory markers that may correlate with FeLV-induced immunosuppression and associated secondary conditions.

[0653] The multispectral sensing device may capture spectral data from the cat's gums, tongue, and conjunctival tissues, analyzing variations in hemoglobin saturation, tissue perfusion, and cellular composition that may be influenced by FeLV infection. In some cases, the virus may cause subtle changes in capillary densify, blood flow patterns, and tissue inflammation that create detectable spectral signatures across the visible and near-infrared wavelength ranges.

[0654] The machine learning algorithms may be trained on datasets correlating multispectral measurements with confirmed FeLV status obtained through traditional testing methods such as ELISA or PCR. The system may learn to recognize spectral patterns associated with the immunosuppressive Attorney Reference No. 444365.000020 effects of FeLV, including changes in tissue oxygenation, inflammatory responses, and secondary infections that commonly occur in FeLV -positive cats.

[0655] In some implementations, the virtual microbiome sequencing capability may provide additional diagnostic insights by detecting microbial imbalances commonly associated with FeLV infection. The system may identify spectral signatures indicating oral dysbiosis or opportunistic infections that frequently develop in immunocompromised cats, providing supporting evidence for potential FeLV infection.

[0656] The system may generate a risk assessment score for FeLV infection based on the combination of spectral biomarkers, tissue health indicators, and predicted microbiome composition. This assessment may flag cats with spectral patterns consistent with FeLV infection for confirmatory laboratory testing while providing immediate guidance on supportive care measures.

[0657] Based on the individual cat's spectral analysis results, the system may recommend targeted therapeutic interventions tailored to the specific health status and detected abnormalities. For cats showing spectral patterns suggestive of FeLV infection, the system may suggest immune-supporting supplements such as lysine, omega-3 fatty acids, or antioxidant complexes that may help support immune function in immunocompromised cats.

[0658] The pharmaceutical recommendation engine may analyze the cat's individual health profile, including age, weight, breed, and detected biomarkers, to suggest appropriate dosages and formulations. In some cases, the system may recommend specific probiotics to address detected oral microbiome imbalances or suggest anti-inflammatory supplements to manage tissue inflammation indicated by the spectral analysis.

[0659] The system may also provide recommendations for environmental modifications and monitoring protocols tailored to cats at risk for or confirmed with FeLV infection. These may include suggestions for stress reduction, dietary modifications to support immune function, and schedules for regular health monitoring using the multispectral device to track treatment response and disease progression.

[0660] In some implementations, the system may correlate the spectral findings with the cat's vaccination history and exposure risks to provide more comprehensive risk assessments. The system may adjust its recommendations based on whether the cat is indoor-only, has outdoor access, or lives in multi-cat households where FeLV transmission risks may be elevated.

[0661] The longitudinal monitoring capabilities may enable tracking of treatment effectiveness over time by analyzing changes in spectral biomarkers, tissue health indicators, and predicted microbiome composition. The system may detect improvements in immune function, reduction in inflammatory markers, or stabilization of tissue health parameters that may indicate successful management of FeLV - related health issues. Attorney Reference No. 444365.000020

[0662] The system may generate comprehensive reports for veterinary review that include the spectral analysis results, FeLV risk assessment, recommended interventions, and monitoring protocols. These reports may facilitate communication between pet owners and veterinarians while providing evidencebased guidance for managing cats with suspected or confirmed FeLV infection.

[0663] The system may include a hand-held or phone-attachable multispectral imaging device including a sensor array with at least eight visible light filters and at least one near-infrared filter, an illumination system, a standoff spacer, and an interface configured to connect to a mobile computing device. Associated software calibrates and reconstructs spectral data from captured images of pet tongue, skin, fur, or mucosa. Machine learning models are applied to derive physiological and biomarker indices from the reconstructed spectral data. The system enables rapid, stress-free health triage with enhanced accessibility through smartphone integration and telehealth capabilities. The technology may be utilized to generate health risk scores, produce shareable reports, and suggest appropriate supplements or pharmaceuticals based on the assessment results. The system may also incorporate virtual microbiome sequencing capabilities that analyze spectral signatures from biological tissues to infer microbial composition without requiring physical biological samples, enabling non-invasive assessment of gut dysbiosis, oral microbial imbalances, and microbiome-related health conditions through detection of metabolic byproducts that influence tissue properties in measurable ways. The system addresses limitations of current invasive or single-band screening methods in veterinary diagnostics by utilizing multispectral imaging technology for comprehensive, non-invasive pet health evaluation. The system may also be integrated with smart water dispensers to provide continuous health monitoring during routine drinking activities, or incorporated into wearable pet collar devices that function as comprehensive activity and health monitoring systems with additional features such as Al-enhanced audio technology for analyzing pet vocalizations and environmental sensors to monitor ambient conditions affecting pet health.

[0664] The methods described herein, including those with reference to one or more flowcharts, may be performed by a controller and/or processing device (e.g., smartphone, computer, augmented and/or virtual reality devices, connected intemet-of-things (“loT”) devices, etc.). The methods may include one or more operations, functions, or actions as illustrated in one or more of blocks. Although the blocks are illustrated in sequential order, these blocks may also be performed in parallel, and/or in a different order than the order disclosed and described herein. Also, the various blocks may be combined into fewer blocks, divided into additional blocks, and/or removed based upon a desired implementation. Dashed lines may represent optional and/or alternative steps.

[0665] Other non-limiting examples may be configured to operate separately or may be combined in any permutation or combination with any one or more of the other examples provided above or Attorney Reference No. 444365.000020 throughout the present disclosure. Components and/or arrangement of components illustrated in one figure may be incorporated into any other figure.

[0666] While the present disclosure has been discussed in terms of certain examples, it should be appreciated that the present disclosure is not so limited. The examples are explained herein are exemplary and there are numerous modifications, variations and other examples that may be employed that would still be within the scope of the present disclosure.

[0667] It will be appreciated by those skilled in the art that the present disclosure may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The presently disclosed examples are therefore considered in all respects to be illustrative and not restricted. The scope of the disclosure is indicated by the appended claims rather than the foregoing description and all changes that come within the meaning and range and equivalence thereof are intended to be embraced therein.

[0668] In general, terminology may be understood at least in part from usage in context. For example, terms, such as “and”, “or”, or “and/or,” as used herein may include a variety of meanings that may depend at least in part upon the context in which such terms are used. Typically, “or” if used to associate a list, such as A, B or C, is intended to mean A, B, and C, here used in the inclusive sense, as well as A, B or C, here used in the exclusive sense. In addition, the term “one or more” as used herein, depending at least in part upon context, may be used to describe any feature, structure, or characteristic in a singular sense or may be used to describe combinations of features, structures or characteristics in a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again, may be understood to convey a singular usage or to convey a plural usage, depending at least in part upon context. In addition, the term “based on” may be understood as not necessarily intended to convey an exclusive set of factors and may, instead, allow for existence of additional factors not necessarily expressly described, again, depending at least in part on context.

[0669] The terms “including” and “comprising” should be interpreted as meaning “including, but not limited to.” If not already set forth explicitly in the claims, the term “a” should be interpreted as “at least one” and the terms “the, said, etc.” should be interpreted as “the at least one, said at least one, etc.” [0670] The present disclosure is described with reference to block diagrams and operational illustrations of methods and devices. It is understood that each block of the block diagrams or operational illustrations, and combinations of blocks in the block diagrams or operational illustrations, may be implemented by means of analog or digital hardware and computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer to alter its function as detailed herein, a special purpose computer, ASIC, or other programmable data processing apparatus, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, implement the functions/acts specified in the block Attorney Reference No. 444365.000020 diagrams or operational block or blocks. In some alternate implementations, the functions/acts noted in the blocks may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality /acts involved.

[0671] For the purposes of this disclosure a non-transitory computer readable medium (or computer- readable storage medium/media) stores computer data, which data may include computer program code (or computer-executable instructions) that is executable by a computer, in machine readable form. By way of example, and not limitation, a computer readable medium may comprise computer readable storage media, for tangible or fixed storage of data, or communication media for transient interpretation of code-containing signals. Computer readable storage media, as used herein, refers to physical or tangible storage (as opposed to signals) and includes without limitation volatile and non-volatile, removable and non-removable media implemented in any method or technology for the tangible storage of information such as computer-readable instructions, data structures, program modules or other data. Computer readable storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, cloud storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other physical or material medium which may be used to tangibly store the desired information or data or instructions and which may be accessed by a computer or processor.

[0672] A computing device may be capable of sending or receiving signals, such as via a wired or wireless network, or may be capable of processing or storing signals, such as in memory as physical memory states, and may, therefore, operate as a server. Thus, devices capable of operating as a server may include, as examples, dedicated rack-mounted servers, desktop computers, laptop computers, set top boxes, integrated devices combining various features, such as two or more features of the foregoing devices, or the like.

[0673] It is the Applicant’s intent that only claims that include the express language “means for” or “step for” be interpreted under 35 U.S.C. 112(f). Claims that do not expressly include the phrase “means for” or “step for” are not to be interpreted under 35 U.S.C. 112(f).

Claims

Attorney Reference No. 444365.000020 CLAIMS What is claimed is:

1. A method for increasing a spectral resolution of a color camera to replicate multispectral, hyperspectral and PPG imaging systems, the method comprising: capturing, by a red-green-blue ("RGB") color image sensor located at a first position, a plurality of color images of one or more objects, each of the plurality of color images having RGB values within the visible spectrum; collecting, by a single pixel detector ("SPD") spectroradiometer located at the first position, measurements for each of the plurality of color images, the measurements comprising spectral values both inside and outside of the visible spectrum; combining each of the plurality of color images and respective measurements to form a training dataset; training, using the training dataset, one or more machine models to associate the RGB values with the respective spectral values both inside and outside of the visible spectrum; and predicting, using the one or more machine models, spectral values both inside and outside of the visible spectrum of one or more additional color images based on RGB values.

2. An apparatus comprising a spectral sensing device incorporating multispectral, hyperspectral (including infrared and ultraviolet), and photoplethysmography (PPG) technologies, configured for use in integrative medicine, dentistry, dermatology, neurology, oncology, ophthalmology, cardiology, psychology, and other medical fields, for diagnosing, monitoring, and preventing conditions by analyzing biological features including at least one of tongue, oral cavity, saliva, teeth, nails, hair, scalp, wrist, breath, pulse, eye, retina, face, reproductive organs, and other anatomical features.

3. A system for predicting a composition of a microbiome, the system comprising: a data interface for receiving multimodal data including at least (i) a spectral image and (ii) a non-spectral image; a processor configured to execute a multimodal neural network with separate branches for each data modality; and a prediction head configured to output a quantitative abundance of at least one target microorganism based on a combined feature vector from the separate branches.

4. A computer-implemented method for generating a health risk assessment, the method comprising: Attorney Reference No. 444365.000020 obtaining a predicted quantitative abundance of at least one microorganism using the system of claim 3; comparing said abundance to a predetermined threshold associated with a medical condition; and generating a risk score for said medical condition based on the comparison.

5. The system of claim 3, wherein the spectral image is a reconstructed image generated by the method of claim 1.

6. The system of claim 3, wherein the target microorganism may include bacteria, fungi, viruses, or other microorganisms present in the microbiome, wherein in some aspects the target microorganism may be a bacterium such as Streptococcus salivarius, Streptococcus parasanguinis, or Porphyromonas gingivalis.

7. The system of claim 3, wherein the system may achieve a Mean Absolute Error (MAE) of less than 0.15 in predicting the abundance of microorganisms, which in some cases may include Streptococcus salivarius.

8. The system of claim 3, wherein the multimodal neural network is a ResNet-inspired architecture with skip connections and batch normalization.

9. The method of claim 4, wherein the medical condition may be associated with microbiome composition, and in some aspects may include Crohn's disease when the microorganism is Streptococcus salivarius.

10. The method of claim 4, wherein the medical condition may be associated with microbiome composition, and in some aspects may include colorectal cancer when the microorganism is Streptococcus parasanguinis.

11. The method of claim 4, wherein the medical condition may be associated with microbiome composition, and in some aspects may include Alzheimer's disease when the microorganism is Porphyromonas gingivalis.

12. The apparatus of claim 2, further comprising one or more NIR sensors centered at approximately 910 nm, capable of performing blood flow monitoring, subcutaneous imaging, and assessing hemoglobin absorption, water content, melanin concentration, gastric motility, fat and muscle Attorney Reference No. 444365.000020 composition, liver health, cardiac output, detection of tumors or abnormal growths, and respiratory function, providing comprehensive insights, prediction, monitoring, and prevention of various physiological conditions.

13. The apparatus of claim 2, wherein the apparatus comprises one or more detachable or standalone sensors, adaptable for integration into rings, bracelets, underwear, VR/AR glasses, bionic lenses, tooth implants, toothbrushes, and other oral hygiene devices.

14. The apparatus of claim 2, wherein the apparatus is capable of 24/7 monitoring and continuous data collection, and may be charged using the body's electricity or by using standard chargers.

15. The apparatus of claim 2, wherein the apparatus comprises a standalone medical device for disease detection, prediction, monitoring or prevention, or in conjunction with accompanying software. The device and the accompanying software may also be used for monitoring, prevention and prediction of disease remission or acute inflammatory states, standalone or used together.

16. The apparatus of claim 2, wherein the apparatus is configured to interface with or integrate into engineered or grown cells and tissues permanently or temporarily entering biological systems, for gathering and sharing information with other systems, as well as with brain computer interface solutions.

17. The apparatus of claim 2, wherein the apparatus is configured to assess and predict a pace of aging, DNA methylation, blood metabolome, biological processes related to aging, hallmarks of aging, organ health and microbiomes, skin, oral and gut microbiome, mycobiome and virome, blood glucose levels, immune system deviations, inflammation, gut microbiome function, breath, tongue, saliva, oral cavity health, and mental, emotional, and cognitive functions.

18. The apparatus of claim 2, configured for use in personalized nutrition, medical therapies, wellness monitoring, early disease detection, and optimization of physical and mental performance.

19. The apparatus of claim 2, wherein the apparatus employs predictive models trained on data obtained from the device, enabling assessments and predictions without the physical presence of the device. Attorney Reference No. 444365.000020

20. The apparatus of claim 2, which leverages sensor technology to replicate functionalities of traditional multispectral and hyperspectral cameras and camera-based PPG sensors, with reduced cost and complexity.

21. The apparatus of claim 2 may be used to scan the oral cavity in a 360-degree manner, with or without the use of a magnifier or fisheye lens as needed, and can be utilized with or without direct contact with the skin, mouth, tongue, or other body parts.

22. The apparatus of claim 2, on its own or through associated software and predictive models, may be integrated into self-driving cars, mirrors, toilets, hotels, smart home systems, fitness equipment, public transportation, personal assistants, augmented reality (AR) and virtual reality (VR) systems, retail environments, healthcare facilities, and entertainment venues, among other applications.

23. The apparatus of claim 2, capable of being used in non-invasive diagnostic applications, including, but not limited to, mapping, predicting and modulating the gut-brain axis, brain default mode network, neural circuits, blood-brain barrier and brain secretome, cognitive function, as well as sociological, psychological, emotional, and behavioral patterns.

24. The system of claim 3, wherein the multimodal data includes a multispectral signature of a tongue, and wherein the system may predict the composition of a stool microbiome based at least in part on the multispectral signature of the tongue.

25. The method of claim 4, wherein the predicted quantitative abundance of at least one microorganism in a stool microbiome may be obtained using a multispectral signature of a tongue as one of the multimodal inputs to the system of Claim 3.

26. The system of claim 3, wherein the system is configured to monitor and predict periodontitis progression through multispectral analysis of inflammatory signatures in oral tissues.

27. The system of claim 26, wherein the multispectral signature of periodontal inflammation includes increased absorption in the 540-580nm range corresponding to elevated hemoglobin concentrations from increased vascularization and blood pooling in inflamed tissues.

28. The system of claim 26, wherein near-infrared spectral features around 760-900nm indicate changes in tissue oxygenation and water content associated with inflammatory edema and altered perfusion patterns. Attorney Reference No. 444365.000020

29. The system of claim 26, wherein inflammatory biomarkers are detected through spectral analysis of the 600-65 Onm range showing characteristic absorption patterns related to inflammatory mediators including prostaglandins and interleukins.

30. The system of claim 26, wherein the 700-75 Onm region reflects changes in tissue structure and collagen degradation associated with periodontal breakdown.

31. The system of claim 26, wherein the system implements machine learning models trained on longitudinal datasets correlating multispectral measurements with clinical periodontal parameters including probing depths, bleeding on probing, and clinical attachment levels.

32. The system of claim 26, wherein the system enables early detection of periodontal inflammation before clinical symptoms become apparent.

33. The system of claim 26, wherein temporal analysis of spectral signatures allows for tracking of disease progression and treatment response over time by establishing baseline spectral profiles for individual patients and monitoring deviations that indicate inflammatory activity or healing responses following periodontal therapy.

34. The method of claim 1, wherein the one or more machine models may be deployed on a smartphone device to predict spectral values from RGB images captured by the smartphone's camera without requiring the physical presence of the SPD spectroradiometer.

35. The method of claim 34, wherein the smartphone device processes the RGB images locally using the trained machine models to generate spectral predictions in real-time.

36. A cloud-based system for spectral analysis, the system comprising: a cloud server configured to receive RGB image data from one or more client devices; processing resources configured to execute machine learning models trained according to the method of claim 1 ; and a communication interface configured to transmit predicted spectral values back to the one or more client devices.

37. The cloud-based system of claim 36, wherein the cloud server may process multiple RGB images simultaneously from different client devices to generate batch spectral predictions. Attorney Reference No. 444365.000020

38. The cloud-based system of claim 36, wherein the machine learning models may be updated remotely without requiring modifications to the client devices.

39. The apparatus of claim 2, wherein the spectral sensing device may comprise a multi -pixel detector array configured to capture spatial and spectral measurements simultaneously.

40. The apparatus of claim 2, wherein the spectral sensing device may comprise a tunable filter system configured to sequentially capture spectral measurements across multiple wavelength ranges.

41. The apparatus of claim 2, wherein the spectral sensing device may comprise an interferometric configuration using Fourier transform infrared (FTIR) or Fabry-Perot interferometer elements to achieve enhanced spectral resolution.

42. The apparatus of claim 2, wherein the spectral sensing device may comprise a hyperspectral line-scan configuration with dispersive optical elements including diffraction gratings or prisms.

43. A method for device-free microbiome analysis, the method comprising: capturing an RGB image of biological tissue using a standard smartphone camera; processing the RGB image using machine learning models trained according to the method of claim 1 to generate predicted spectral values; analyzing the predicted spectral values using the multimodal neural network of claim 3; and outputting a predicted microbiome composition based on the analysis.

44. The method of claim 43, wherein the RGB image may be processed locally on the smartphone device or transmitted to a cloud-based processing system.

45. The method of claim 43, wherein the machine learning models may compensate for smartphone camera processing effects including white balance adjustments and high dynamic range imaging.

46. A distributed sensing system comprising: a plurality of spectral sensing devices according to claim 2 positioned at different locations; a central processing unit configured to aggregate spectral data from the plurality of devices; and Attorney Reference No. 444365.000020 analysis software configured to perform population-level health monitoring based on the aggregated data.

47. The distributed sensing system of claim 46, wherein the plurality of spectral sensing devices may communicate wirelessly with the central processing unit through cellular, Wi-Fi, or Bluetooth connectivity.

48. The apparatus of claim 2, wherein the spectral sensing device may be configured as a modular attachment that interfaces with existing electronic devices through standard connection protocols including USB, Lightning, or wireless interfaces.

49. The apparatus of claim 2, wherein the spectral sensing device may comprise software-defined sensor configurations that may be reconfigured for different spectral sensing applications through downloadable software updates.

50. The system of claim 3, wherein the multimodal neural network may be implemented using edge computing hardware including graphics processing units, tensor processing units, or field- programmable gate arrays to provide local processing capabilities.

51. The system of claim 3, wherein the multimodal neural network may be configured to process temporal sequences of spectral measurements to identify dynamic changes in microbiome composition over extended monitoring periods.

52. A method for analyzing tongue images using spectral reconstruction, comprising: capturing, using a consumer camera device, at least one RGB image of a subject's protruding tongue; processing the RGB image using trained machine learning models to reconstruct spectral values both inside and outside of the visible spectrum from the RGB image data; analyzing the reconstructed spectral values to identify specific spectral signatures associated with microbial abundance and metabolic markers; and generating, based on the identified spectral signatures, predicted health indicators including at least one of: oral microbiome composition, nutrient deficiency indicators derived from spectral absorption patterns, metabolic efficiency markers based on spectral biomarkers, and disease risk assessments correlated with spectral characteristics. Attorney Reference No. 444365.000020

53. The method of claim 52, wherein the spectral signatures include near-infrared absorption patterns indicative of hemoglobin levels, water content variations, and bacterial metabolite concentrations in tongue tissue.

54. The method of claim 52, wherein the nutrient deficiency indicators are determined by analyzing spectral absorption characteristics at specific wavelengths associated with vitamin B12, iron, folate, and vitamin D deficiency manifestations in tongue tissue.

55. The method of claim 52, wherein the microbiome composition is predicted by correlating reconstructed spectral signatures with known spectral fingerprints of specific bacterial species.

56. A method comprising: capturing a selfie-style tongue image using a smartphone camera; reconstructing multispectral data from the RGB image using the method of claim 1 ; and automatically generating health predictions based solely on spectral analysis of the reconstructed data.

57. A hand-held or phone-attachable multispectral imaging device comprising: a sensor array comprising at least eight visible light filters and at least one near-infrared filter; an illumination system; a standoff spacer; and an interface configured to connect to a mobile computing device.

58. The device of claim 57, wherein the near-infrared filter is centered at 910 nm.

59. The device of claim 57, wherein the illumination system comprises polarized light sources to reduce fur and glint artifacts.

60. The device of claim 57, further comprising a processor configured to execute instructions for spectral reconstruction.

61. The device of claim 60, wherein the instructions for spectral reconstruction comprise a machine learning ensemble combining physics-based spectral reconstruction and data-driven models. Attorney Reference No. 444365.000020

62. The device of claim 57, wherein the interface comprises a wireless connection to the mobile computing device.

63. The device of claim 57, wherein the standoff spacer is adjustable to accommodate different animal sizes.

64. The device of claim 57, further comprising a temperature sensor.

65. The device of claim 57, wherein the sensor array comprises filters centered at wavelengths optimized for detecting hemoglobin and melanin.

66. The device of claim 57, further comprising a color calibration target integrated into the standoff spacer.

67. The device of claim 66, wherein the color calibration target enables a color-correction pipeline achieving a specified AE value.

68. The device of claim 60, wherein the processor is further configured to execute instructions for removing motion artifacts from captured images.

69. A system for non-invasive animal health assessment comprising: the hand-held or phone-attachable multispectral imaging device of claim 57; a mobile device camera; and software configured to: acquire synchronized RGB and multispectral frames, apply calibration to the acquired frames, and compute biomarker indices specific to non-human mammals.

70. The system of claim 69, wherein the software is further configured to detect and analyze biomarkers including tongue pallor as an indicator for anemia risk.

71. The system of claim 70, wherein the software is further configured to detect and analyze gingival color as an indicator for hydration or perfusion. Attorney Reference No. 444365.000020

72. The system of claim 71, wherein the software is further configured to detect and analyze facial thermal patterns in Near-Infrared (NIR) as an indicator for inflammation.

73. The system of claim 72, wherein the software is further configured to detect and analyze coat lustre index as an indicator for nutritional status or endocrine disorders.

74. The system of claim 69, wherein the software is further configured to generate risk scores based on the computed biomarker indices.

75. The system of claim 74, wherein the software is further configured to generate trend graphs visualizing changes in the computed biomarker indices over time.

76. The system of claim 75, wherein the software is further configured to generate a veterinarian- shareable report that is FHIR compliant.

77. The system of claim 69, wherein the software is further configured to implement a cleaning protocol for the multispectral imaging device.

78. The system of claim 69, wherein the software is further configured to adapt to various animal mucosa pigments for reference.

79. The system of claim 69, wherein the software is further configured to generate supplement and pharmaceutical recommendations based on the computed biomarker indices.

80. The system of claim 79, wherein the supplement and pharmaceutical recommendations are tailored to specific pet species and breeds.

81. A method of non-invasively assessing animal health comprising: positioning a multispectral imaging device relative to an animal; illuminating a region of interest on the animal; capturing multispectral data of the illuminated region; reconstructing spectral signatures from the captured multispectral data; computing at least one physiological indicator based on the reconstructed spectral signatures; outputting a health report including the at least one physiological indicator; and Attorney Reference No. 444365.000020 automatically adjusting at least one of illumination intensity, spectral filter selection, or image capture settings of the multispectral imaging device based on the computed physiological indicator to optimize subsequent multispectral data capture.

82. The method of claim 81, wherein the region of interest comprises at least one of a tongue, gums, or eyes of the animal.

83. The method of claim 82, wherein illuminating the region of interest comprises using polarized light sources to reduce fur and glint artifacts.

84. The method of claim 83, wherein capturing multispectral data comprises acquiring synchronized RGB and multispectral frames.

85. The method of claim 84, further comprising applying a calibration to the acquired frames prior to reconstructing spectral signatures.

86. The method of claim 85, wherein reconstructing spectral signatures comprises using a machine learning ensemble combining physics-based spectral reconstruction and data-driven models.

87. The method of claim 86, wherein computing the at least one physiological indicator comprises detecting and analyzing tongue pallor as an indicator for anemia risk.

88. The method of claim 87, wherein computing the at least one physiological indicator further comprises detecting and analyzing gingival color as an indicator for hydration or perfusion.

89. The method of claim 88, wherein computing the at least one physiological indicator further comprises detecting and analyzing facial thermal patterns in Near-Infrared (NIR) as an indicator for inflammation.

90. The method of claim 89, wherein computing the at least one physiological indicator further comprises detecting and analyzing coat lustre index as an indicator for nutritional status or endocrine disorders.

91. The method of claim 90, wherein outputting the health report comprises generating risk scores based on the computed physiological indicators. Attorney Reference No. 444365.000020

92. The method of claim 91, wherein outputting the health report further comprises generating trend graphs visualizing changes in the computed physiological indicators over time.

93. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method of non-invasively assessing animal health, the method comprising: receiving multispectral data captured from an animal; reconstructing spectral signatures from the received multispectral data; computing at least one physiological indicator based on the reconstructed spectral signatures; and generating a health report including the at least one physiological indicator.

94. The non-transitory computer-readable medium of claim 93, wherein reconstructing spectral signatures comprises using a machine learning ensemble combining physics-based spectral reconstruction and data-driven models.

95. The non-transitory computer-readable medium of claim 94, wherein the machine learning ensemble includes a random forest model for color prediction.

96. The non-transitory computer-readable medium of claim 95, wherein the machine learning ensemble further includes a convolutional neural network for processing and analyzing multispectral images.

97. The non-transitory computer-readable medium of claim 93, wherein computing the at least one physiological indicator comprises detecting and analyzing tongue pallor as an indicator for anemia risk.

98. The non-transitory computer-readable medium of claim 97, wherein computing the at least one physiological indicator further comprises detecting and analyzing gingival color as an indicator for hydration or perfusion.

99. The non-transitory computer-readable medium of claim 98, wherein computing the at least one physiological indicator further comprises detecting and analyzing facial thermal patterns in NearInfrared (NIR) as an indicator for inflammation. Attorney Reference No. 444365.000020

100. The non-transitory computer-readable medium of claim 99, wherein computing the at least one physiological indicator further comprises detecting and analyzing coat lustre index as an indicator for nutritional status or endocrine disorders.

101. The non-transitory computer-readable medium of claim 93, wherein generating the health report comprises generating risk scores based on the computed physiological indicators.

102. The non-transitory computer-readable medium of claim 101, wherein generating the health report further comprises generating trend graphs visualizing changes in the computed physiological indicators over time.

103. The non-transitory computer-readable medium of claim 102, wherein the health report is FHIR compliant.

104. The non-transitory computer-readable medium of claim 93, wherein the method further comprises generating supplement and pharmaceutical recommendations based on the computed physiological indicators.

105. The non-transitory computer-readable medium of claim 104, wherein the supplement and pharmaceutical recommendations are tailored to specific pet species and breeds.

106. The non-transitory computer-readable medium of claim 93, wherein the method further comprises implementing a cleaning protocol for a multispectral imaging device used to capture the multispectral data.

107. The device of claim 57, wherein the sensor array achieves color reconstruction accuracy of at least 95% with a correlation coefficient of at least 0.99 for at least one wavelength range.

108. The device of claim 60, wherein the instructions for spectral reconstruction comprise a Random Forest model for color prediction and a Convolutional Neural Network for processing and analyzing multispectral images.

109. The system of claim 76, wherein the FHIR-compliant report includes standardized data fields for pet health information and diagnostic results. Attorney Reference No. 444365.000020

110. The system of claim 69, wherein the software is further configured to operate in multiple modes including at-home monitoring, tele-triage, and in-clinic quick scan.

111. The system of claim 79, wherein the supplement and pharmaceutical recommendations are generated using a machine learning algorithm trained on historical multispectral data and health outcomes.

112. The device of claim 57, wherein the illumination system comprises silent light-emitting diodes and the sensor array is configured to capture multispectral data in less than 1 second.

113. The system of claim 69, further comprising an interface module configured to integrate with existing veterinary electronic health record systems.

114. The system of claim 78, wherein the software includes species-specific calibration settings for at least dogs, cats, and small mammals.

115. The device of claim 57, wherein the near-infrared filter is centered at a wavelength between 800 nm and 1000 nm.

116. The system of claim 69, wherein the software includes algorithms for detecting and analyzing at least five distinct biomarkers associated with pet health conditions.

117. The system of claim 79, further comprising generating personalized pet health recommendations by: analyzing the multispectral data using machine learning algorithms to identify pet-specific health indicators; comparing the identified health indicators to a database of pet health conditions and treatments; and generating personalized supplement, pharmaceutical and health management recommendations based on the comparison.

118. The system of claim 69, wherein the software is further configured to generate a user interface for displaying real-time multispectral analysis results during a veterinary examination. Attorney Reference No. 444365.000020

119. The device of claim 57, further comprising a proximity sensor configured to automatically trigger multispectral data capture when the device is positioned at an optimal distance from an animal.

120. The system of claim 69, wherein the software is further configured to implement data encryption protocols for securing pet health information during transmission and storage.

121. The method of claim 81, further comprising: storing the computed physiological indicators in a longitudinal health record; and analyzing trends in the longitudinal health record to predict future health risks.

122. The system of claim 117, wherein analyzing the multispectral data comprises detecting and quantifying at least four biomarkers selected from the group consisting of tongue pallor, gingival color, facial thermal patterns, and coat lustre index.

123. The system of claim 117, further comprising: receiving individual pet characteristics including age, weight, and existing health conditions; and adjusting the personalized supplement and pharmaceutical recommendations based on the received individual pet characteristics.

124. The system of claim 117, further comprising: accessing veterinary electronic health records associated with the pet; integrating data from the veterinary electronic health records with the identified pet-specific health indicators; and refining the personalized supplement and pharmaceutical recommendations based on the integrated data.

125. The system of claim 117, further comprising: storing the multispectral data and generated recommendations in a longitudinal health record; analyzing trends in the longitudinal health record over time; and updating the personalized supplement and pharmaceutical recommendations based on the analyzed trends.

126. The system of claim 117, wherein the personalized health management recommendations include at least one category selected from nutritional guidance, activity recommendations, environmental modifications, veterinary care scheduling, nutritional supplements, vitamins, Attorney Reference No. 444365.000020 minerals, probiotics, joint health supplements, skin and coat health products, and condition-specific medications.

127. The system of claim 123, further comprising: retrieving information on existing medications prescribed to the pet; analyzing potential interactions between the existing medications and the generated supplement and pharmaceutical recommendations; and adjusting the recommendations to minimize potential adverse interaction and facilitate integration with existing protocols.

128. The system of claim 117, further comprising generating a report for presentation to pet owners or veterinarians, wherein the report includes: a list of recommended supplements and pharmaceuticals; explanatory notes detailing the rationale for each recommendation; potential benefits and risks associated with each recommendation; and suggested dosage and administration instructions.

129. The system of claim 69, wherein for pets with multiple health issues, the software is further configured to: apply optimization algorithms to generate a comprehensive treatment plan; balance the efficacy of different interventions; minimize potential side effects and adverse interactions; prioritize recommendations based on their potential impact on overall pet health; and generate an integrated health status report.

130. A smart water dispenser system comprising: a water dispensing unit; embedded multispectral imaging sensors positioned to capture spectral data from a pet's tongue, gums, and oral cavity during drinking activities; a sensor array comprising at least eight visible light filters and at least one near-infrared filter; adaptive illumination systems configured to activate when motion sensors detect a pet approaching the water bowl; and a processor configured to analyze the captured spectral data to generate health assessments. Attorney Reference No. 444365.000020

131. The smart water dispenser system of claim 130, wherein the illumination systems are configured to be non-intrusive and silent to avoid disrupting natural drinking behavior.

132. The smart water dispenser system of claim 130, further comprising flow sensors configured to correlate drinking patterns with health indicators.

133. The smart water dispenser system of claim 132, wherein the processor is configured to analyze relationships between water consumption rates, drinking frequency, and spectral biomarker measurements.

134. The smart water dispenser system of claim 130, wherein the processor is configured to track hydration-related biomarkers, gingival color changes, and tongue pallor indicators over time.

135. The smart water dispenser system of claim 130, wherein the system is configured to perform automated health assessments multiple times per day based on natural drinking frequency.

136. A wearable pet collar device comprising: miniaturized multispectral sensing components configured for continuous or periodic health measurements; accelerometers, gyroscopes, and GPS sensors configured to capture movement metrics including step counts, activity levels, sleep patterns, and location tracking; a processor configured to correlate movement data with multispectral health measurements; and wireless connectivity capabilities for transmitting collected data to external devices.

137. The wearable pet collar device of claim 136, wherein the multispectral sensing components are configured to activate at predetermined intervals to capture spectral data from accessible areas where the collar contacts the pet's skin.

138. The wearable pet collar device of claim 136, further comprising retractable or adjustable sensor components positionable for optimal spectral data capture during rest periods.

139. The wearable pet collar device of claim 136, wherein the processor is configured to analyze gait patterns, exercise intensity, and rest periods to provide comprehensive activity profiles. Attorney Reference No. 444365.000020

140. The wearable pet collar device of claim 136, further comprising Al-enhanced audio technology configured to detect and analyze pet vocalizations.

141. The wearable pet collar device of claim 140, wherein the audio technology is configured to capture and process barks, whines, purrs, and other vocalizations.

142. The wearable pet collar device of claim 140, wherein Al algorithms are trained to recognize changes in vocalization patterns that may indicate emotional stress, pain, or other health-related conditions.

143. The wearable pet collar device of claim 140, wherein the processor is configured to correlate changes in vocalization patterns with spectral biomarker measurements.

144. The wearable pet collar device of claim 140, further comprising audio playback capabilities configured to project familiar sounds including recorded owner voices.

145. The wearable pet collar device of claim 144, wherein the system is configured to automatically detect signs of separation anxiety and trigger playback of comforting audio content.

146. The wearable pet collar device of claim 144, wherein the processor is configured to monitor changes in stress indicators to evaluate effectiveness of audio comfort features.

147. The wearable pet collar device of claim 136, further comprising adaptive power management systems configured to adjust sampling rates and sensor activation based on activity levels and detected health status.

148. The wearable pet collar device of claim 136, further comprising environmental sensors configured to monitor temperature, humidity, air quality, and UV exposure levels.

149. The wearable pet collar device of claim 148, wherein the processor is configured to correlate environmental data with health measurements to identify potential environmental factors contributing to health changes.

150. The wearable pet collar device of claim 136, wherein the processor is configured to generate comprehensive reports combining activity metrics, health indicators, and behavioral patterns. Attorney Reference No. 444365.000020

151. The wearable pet collar device of claim 136, further comprising customizable alert systems tailored to individual pet needs and owner preferences.

152. The wearable pet collar device of claim 151, wherein the system is configured to learn normal patterns for each pet and provide personalized alerts when deviations from typical behavior or health indicators are detected.

153. The wearable pet collar device of claim 136, wherein the processor incorporates machine learning algorithms configured to analyze patterns in both activity data and spectral measurements to identify potential health issues.

154. The wearable pet collar device of claim 136, wherein the device comprises modular components configured to be upgraded or replaced as technology advances.

155. A multi-modal pet health monitoring system comprising: the smart water dispenser system of claim 130; and the wearable pet collar device of claim 136; wherein the system is configured to provide continuous health monitoring through multiple data collection points.

156. The multi-modal pet health monitoring system of claim 155, wherein the system is configured to support longitudinal health studies by continuously collecting diverse data types over extended periods.

157. The multi-modal pet health monitoring system of claim 155, wherein the system includes machine learning capabilities configured to continuously improve accuracy and effectiveness based on accumulated data from multiple pets.

158. The multi-modal pet health monitoring system of claim 155, wherein the system is configured to identify population-level health trends and patterns to inform individual pet care recommendations and veterinary best practices. Attorney Reference No. 444365.000020

159. The system of claim 69, wherein the software is further configured to perform virtual microbiome sequencing by analyzing spectral signatures from biological tissues to infer microbial composition without requiring physical biological samples.

160. The system of claim 159, wherein the virtual microbiome sequencing comprises: detecting metabolic byproducts from microbial communities that cause changes in tissue inflammation, perfusion, and cellular composition; and identifying unique spectral signatures associated with the detected changes using the multispectral and near-infrared sensors.

161. The system of claim 160, wherein the metabolic byproducts originate from gut dysbiosis or oral microbial imbalances and enter systemic circulation to influence tissue properties in measurable ways.

162. The system of claim 159, wherein the software comprises a machine learning model trained on a dataset correlating multispectral spectral data with ground-truth microbiome data obtained from laboratory -based sequencing methods.

163. The system of claim 162, wherein the ground-truth microbiome data comprises at least one of 16S rRNA sequencing data and shotgun metagenomic sequencing data.

164. The system of claim 162, wherein the machine learning model comprises a deep neural network configured to identify correlations between high-resolution spectral data and specific microbial community compositions.

165. The system of claim 164, wherein the deep neural network incorporates convolutional layers designed to identify complex patterns within multispectral data that correspond to specific microbial signatures.

166. The system of claim 162, wherein the machine learning model incorporates attention mechanisms to focus on spectral features most relevant to microbial activity.

167. The system of claim 159, wherein the virtual microbiome sequencing generates a predictive microbiome profile identifying likely abundance of key bacterial phyla in the pet's microbiome. Attorney Reference No. 444365.000020

168. The system of claim 167, wherein the key bacterial phyla comprise at least Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria.

169. The system of claim 167, wherein the predictive microbiome profile includes confidence intervals for each bacterial phyla abundance prediction.

170. The system of claim 159, wherein the virtual microbiome sequencing comprises dysbiosis detection capabilities that flag potential microbial imbalances based on spectral analysis.

171. The system of claim 170, wherein the dysbiosis detection identifies patterns associated with at least one condition selected from small intestinal bacterial overgrowth, inflammatory bowel conditions, and oral microbiome disruptions.

172. The system of claim 159, wherein the virtual microbiome sequencing generates output formatted similarly to traditional microbiome sequencing reports, including relative abundance charts, diversity indices, and comparisons to healthy reference populations.

173. The system of claim 172, wherein the output includes species-specific reference ranges based on the pet's breed, age, and other relevant characteristics.

174. The system of claim 159, wherein the virtual microbiome sequencing incorporates temporal analysis features that track changes in predicted microbiome composition over time.

175. The system of claim 174, wherein the temporal analysis enables detection of microbiome shifts associated with dietary changes, antibiotic treatments, or developing health conditions.

176. The system of claim 159, wherein the software correlates virtual microbiome data with other health indicators captured by the multispectral analysis to provide comprehensive health assessments.

177. The system of claim 176, wherein the other health indicators comprise inflammation indicators, nutritional status markers, and immune function assessments. Attorney Reference No. 444365.000020

178. The system of claim 159, wherein the virtual microbiome sequencing includes quality control measures that provide confidence scores for microbiome estimates and flag cases where spectral data quality falls outside training dataset parameters.

179. The system of claim 159, wherein the machine learning model is configured for continuous improvement through federated learning approaches using anonymized data from multiple users.

180. The system of claim 159, wherein the virtual microbiome sequencing integrates with treatment planning by suggesting microbiome-targeted interventions based on predicted microbial composition.

181. The system of claim 180, wherein the microbiome -targeted interventions comprise at least one selected from specific probiotics, prebiotics, and dietary modifications tailored to address identified dysbiotic patterns.

182. The system of claim 159, wherein the virtual microbiome sequencing monitors treatment effectiveness by tracking predicted microbiome changes in response to interventions over time.

183. The system of claim 159, wherein the software incorporates species-specific microbiome models that account for natural variations in microbial communities across different pet species.

184. The system of claim 183, wherein the species-specific models are calibrated for at least dogs, cats, and other companion animals.

185. The system of claim 159, wherein the virtual microbiome sequencing includes functional prediction capabilities that estimate metabolic potential of the predicted microbial community.

186. The system of claim 185, wherein the functional predictions comprise insights into at least one microbial function selected from short-chain fatty acid production, vitamin synthesis, and xenobiotic metabolism.

187. The system of claim 159, wherein the virtual microbiome data integrates with the supplement and pharmaceutical recommendation engine to suggest interventions specifically targeted at microbiome optimization. Attorney Reference No. 444365.000020

188. The system of claim 187, wherein the microbiome optimization interventions comprise microbiome-supporting supplements and identification of potential medication interactions that could disrupt microbial balance.

189. A method of virtual microbiome sequencing comprising: capturing multispectral data from biological tissues of a pet using a multispectral sensing device; analyzing the multispectral data to detect spectral signatures associated with metabolic byproducts from microbial communities; applying a machine learning model trained on correlations between spectral data and groundtruth microbiome data to generate a predictive microbiome profile; and outputting the predictive microbiome profile identifying likely abundance of key bacterial phyla and potential dysbiosis indicators.

190. The method of claim 189, wherein detecting spectral signatures comprises identifying changes in tissue inflammation, perfusion, and cellular composition caused by metabolic byproducts from gut dysbiosis or oral microbial imbalances.

191. The method of claim 189, wherein the machine learning model comprises a deep neural network trained on a dataset of paired multispectral measurements and laboratory-based microbiome sequencing results.

192. The method of claim 189, further comprising tracking changes in predicted microbiome composition over time to monitor treatment effectiveness or detect developing health conditions.

193. The method of claim 189, further comprising correlating the predictive microbiome profile with other health indicators to generate comprehensive health assessments.

194. The method of claim 189, further comprising generating microbiome-targeted intervention recommendations based on the predictive microbiome profile.

195. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform virtual microbiome sequencing, the method comprising: receiving multispectral data captured from biological tissues of a pet; analyzing the multispectral data to identify spectral signatures associated with microbial metabolic byproducts;

I l l Attorney Reference No. 444365.000020 applying a trained machine learning model to correlate the spectral signatures with predicted microbiome composition; and generating a virtual microbiome profile identifying likely bacterial phyla abundances and dysbiosis indicators.

196. The non-transitory computer-readable medium of claim 195, wherein the trained machine learning model comprises a deep neural network trained on paired datasets of multispectral measurements and ground-truth microbiome sequencing data.

197. The system of claim 69, wherein the software is further configured to detect spectral signatures associated with feline leukemia virus (FeLV) infection through analysis of oral and ocular tissues.

198. The system of claim 197, wherein the software identifies characteristic changes in tissue oxygenation, perfusion patterns, and inflammatory markers that may correlate with FeLV-induced immunosuppression and associated secondary conditions.

199. The system of claim 197, wherein the multispectral sensing device captures spectral data from at least one tissue selected from gums, tongue, and conjunctival tissues to analyze variations in hemoglobin saturation, tissue perfusion, and cellular composition.

200. The system of claim 197, wherein the machine learning algorithms are trained on datasets correlating multispectral measurements with confirmed FeLV status obtained through at least one method selected from ELISA and PCR testing.

201. The system of claim 197, wherein the software recognizes spectral patterns associated with immunosuppressive effects of FeLV, including changes in tissue oxygenation, inflammatory responses, and secondary infections.

202. The system of claim 159, wherein the virtual microbiome sequencing capability provides diagnostic insights by detecting microbial imbalances associated with FeLV infection.

203. The system of claim 202, wherein the software identifies spectral signatures indicating oral dysbiosis or opportunistic infections that develop in immunocompromised cats. Attorney Reference No. 444365.000020

204. The system of claim 197, wherein the software generates a risk assessment score for FeLV infection based on a combination of spectral biomarkers, tissue health indicators, and predicted microbiome composition.

205. The system of claim 204, wherein the risk assessment score flags cats with spectral patterns consistent with FeLV infection for confirmatory laboratory testing.

206. The system of claim 79, wherein the supplement and pharmaceutical recommendations include FeLV-specific interventions for cats showing spectral patterns suggestive of FeLV infection.

207. The system of claim 206, wherein the FeLV-specific interventions comprise at least one selected from immune-supporting supplements, lysine, omega-3 fatty acids, and antioxidant complexes.

208. The system of claim 206, wherein the pharmaceutical recommendation engine analyzes individual cat characteristics including age, weight, and breed to suggest appropriate dosages and formulations for FeLV -related interventions.

209. The system of claim 206, wherein the software recommends specific probiotics to address detected oral microbiome imbalances or anti-inflammatory supplements to manage tissue inflammation indicated by spectral analysis.

210. The system of claim 197, wherein the software provides recommendations for environmental modifications and monitoring protocols tailored to cats at risk for or confirmed with FeLV infection.

211. The system of claim 210, wherein the recommendations include suggestions for stress reduction, dietary modifications to support immune function, and schedules for regular health monitoring using the multispectral device.

212. The system of claim 197, wherein the software correlates spectral findings with vaccination history and exposure risks to provide comprehensive FeLV risk assessments.

213. The system of claim 212, wherein the software adjusts recommendations based on whether the cat is indoor-only, has outdoor access, or lives in multi-cat households where FeLV transmission risks may be elevated. Attorney Reference No. 444365.000020

214. The system of claim 197, wherein the longitudinal monitoring capabilities enable tracking of treatment effectiveness over time by analyzing changes in spectral biomarkers, tissue health indicators, and predicted microbiome composition.

215. The system of claim 214, wherein the software detects improvements in immune function, reduction in inflammatory markers, or stabilization of tissue health parameters that may indicate successful management of FeLV-related health issues.

216. The system of claim 197, wherein the software generates comprehensive reports for veterinary review that include spectral analysis results, FeLV risk assessment, recommended interventions, and monitoring protocols.